From b1b074780a212524a8382bf50a03f6b2579be652 Mon Sep 17 00:00:00 2001 From: felix Date: Tue, 27 Oct 2026 09:19:45 +0100 Subject: [PATCH] [docs] Refactor documentation --- .codacy.yml | 2 +- .gitignore | 2 + .pre-commit-config.yaml | 2 +- README.md | 4 +- docs/README.md | 88 +- docs/build.sh | 76 +- docs/source/_includes/pipeline.html | 67 ++ docs/source/_static/css/doctr.css | 1022 +++++++++++++++++ docs/source/_static/css/mindee.css | 52 - docs/source/_static/fonts/LICENSE-Inter.txt | 93 ++ .../_static/fonts/LICENSE-JetBrainsMono.txt | 93 ++ .../fonts/inter-latin-ext-wght-normal.woff2 | Bin 0 -> 85068 bytes .../fonts/inter-latin-wght-normal.woff2 | Bin 0 -> 48256 bytes ...jetbrains-mono-latin-ext-wght-normal.woff2 | Bin 0 -> 15196 bytes .../jetbrains-mono-latin-wght-normal.woff2 | Bin 0 -> 40404 bytes .../_static/images/Logo-docTR-white.png | Bin 7568 -> 0 bytes docs/source/_static/images/demo.png | Bin 0 -> 177099 bytes .../_static/images/examples/invoice.pdf | Bin 0 -> 104294 bytes .../_static/images/examples/invoice_input.png | Bin 0 -> 90932 bytes .../images/examples/invoice_layout.png | Bin 0 -> 100097 bytes .../images/examples/invoice_synthesized.png | Bin 0 -> 84040 bytes .../_static/images/examples/invoice_words.png | Bin 0 -> 98225 bytes docs/source/_static/images/logo-dark.png | Bin 0 -> 5337 bytes docs/source/_static/images/logo-light.png | Bin 0 -> 5321 bytes docs/source/_static/js/custom.js | 104 -- docs/source/_static/js/doctr.js | 15 + docs/source/_static/switcher.json | 33 + docs/source/_templates/license.html | 3 + docs/source/_templates/maintainers.html | 4 + docs/source/api/contrib.md | 13 + docs/source/api/datasets.md | 93 ++ docs/source/api/index.md | 61 + docs/source/api/io.md | 159 +++ .../{modules/models.rst => api/models.md} | 194 ++-- docs/source/api/transforms.md | 70 ++ docs/source/api/utils.md | 53 + docs/source/changelog.md | 27 + docs/source/changelog.rst | 86 -- docs/source/community/index.md | 114 ++ docs/source/community/notebooks.rst | 1 + docs/source/community/resources.md | 43 + docs/source/community/resources.rst | 23 - docs/source/community/tools.md | 41 + docs/source/community/tools.rst | 90 -- docs/source/conf.py | 374 ++++-- docs/source/deployment/configuration.md | 64 ++ docs/source/deployment/docker.md | 59 + docs/source/deployment/index.md | 52 + docs/source/deployment/onnx.md | 74 ++ docs/source/deployment/performance.md | 136 +++ docs/source/deployment/web_api.md | 80 ++ docs/source/getting_started/concepts.md | 176 +++ docs/source/getting_started/index.md | 43 + docs/source/getting_started/installation.md | 134 +++ docs/source/getting_started/installing.rst | 52 - docs/source/getting_started/quickstart.md | 249 ++++ docs/source/getting_started/quickstart.rst | 110 -- docs/source/index.md | 194 ++++ docs/source/index.rst | 127 -- docs/source/models/detection.md | 49 + docs/source/models/end_to_end.md | 31 + docs/source/models/index.md | 71 ++ docs/source/models/layout.md | 36 + docs/source/models/recognition.md | 55 + docs/source/models/table_structure.md | 34 + docs/source/models/vocabs.md | 34 + docs/source/modules/contrib.rst | 13 - docs/source/modules/datasets.rst | 404 ------- docs/source/modules/io.rst | 240 ---- docs/source/modules/transforms.rst | 39 - docs/source/modules/utils.rst | 67 -- docs/source/notebooks.rst | 1 - docs/source/training/custom_models.md | 175 +++ docs/source/training/datasets.md | 137 +++ docs/source/training/index.md | 63 + docs/source/training/sharing_models.md | 70 ++ docs/source/training/train.md | 233 ++++ docs/source/user_guide/artefacts.md | 56 + docs/source/user_guide/cli.md | 135 +++ docs/source/user_guide/exporting.md | 210 ++++ docs/source/user_guide/faq.md | 112 ++ docs/source/user_guide/index.md | 124 ++ docs/source/user_guide/kie.md | 73 ++ docs/source/user_guide/layout_and_tables.md | 177 +++ docs/source/user_guide/loading_documents.md | 131 +++ docs/source/user_guide/ocr_predictor.md | 323 ++++++ docs/source/user_guide/results.md | 234 ++++ docs/source/user_guide/rotated_documents.md | 94 ++ .../user_guide/standalone_predictors.md | 96 ++ .../using_doctr/custom_models_training.rst | 221 ---- docs/source/using_doctr/running_on_aws.rst | 10 - docs/source/using_doctr/sharing_models.rst | 127 -- docs/source/using_doctr/using_cli.rst | 124 -- .../using_doctr/using_contrib_modules.rst | 51 - docs/source/using_doctr/using_datasets.rst | 188 --- .../source/using_doctr/using_model_export.rst | 151 --- docs/source/using_doctr/using_models.rst | 836 -------------- doctr/utils/metrics.py | 3 +- pyproject.toml | 28 +- 99 files changed, 6564 insertions(+), 3344 deletions(-) create mode 100644 docs/source/_includes/pipeline.html create mode 100644 docs/source/_static/css/doctr.css delete mode 100644 docs/source/_static/css/mindee.css create mode 100644 docs/source/_static/fonts/LICENSE-Inter.txt create mode 100644 docs/source/_static/fonts/LICENSE-JetBrainsMono.txt create mode 100644 docs/source/_static/fonts/inter-latin-ext-wght-normal.woff2 create mode 100644 docs/source/_static/fonts/inter-latin-wght-normal.woff2 create mode 100644 docs/source/_static/fonts/jetbrains-mono-latin-ext-wght-normal.woff2 create mode 100644 docs/source/_static/fonts/jetbrains-mono-latin-wght-normal.woff2 delete mode 100644 docs/source/_static/images/Logo-docTR-white.png create mode 100644 docs/source/_static/images/demo.png create mode 100644 docs/source/_static/images/examples/invoice.pdf create mode 100644 docs/source/_static/images/examples/invoice_input.png create mode 100644 docs/source/_static/images/examples/invoice_layout.png create mode 100644 docs/source/_static/images/examples/invoice_synthesized.png create mode 100644 docs/source/_static/images/examples/invoice_words.png create mode 100644 docs/source/_static/images/logo-dark.png create mode 100644 docs/source/_static/images/logo-light.png delete mode 100644 docs/source/_static/js/custom.js create mode 100644 docs/source/_static/js/doctr.js create mode 100644 docs/source/_static/switcher.json create mode 100644 docs/source/_templates/license.html create mode 100644 docs/source/_templates/maintainers.html create mode 100644 docs/source/api/contrib.md create mode 100644 docs/source/api/datasets.md create mode 100644 docs/source/api/index.md create mode 100644 docs/source/api/io.md rename docs/source/{modules/models.rst => api/models.md} (65%) create mode 100644 docs/source/api/transforms.md create mode 100644 docs/source/api/utils.md create mode 100644 docs/source/changelog.md delete mode 100644 docs/source/changelog.rst create mode 100644 docs/source/community/index.md create mode 120000 docs/source/community/notebooks.rst create mode 100644 docs/source/community/resources.md delete mode 100644 docs/source/community/resources.rst create mode 100644 docs/source/community/tools.md delete mode 100644 docs/source/community/tools.rst create mode 100644 docs/source/deployment/configuration.md create mode 100644 docs/source/deployment/docker.md create mode 100644 docs/source/deployment/index.md create mode 100644 docs/source/deployment/onnx.md create mode 100644 docs/source/deployment/performance.md create mode 100644 docs/source/deployment/web_api.md create mode 100644 docs/source/getting_started/concepts.md create mode 100644 docs/source/getting_started/index.md create mode 100644 docs/source/getting_started/installation.md delete mode 100644 docs/source/getting_started/installing.rst create mode 100644 docs/source/getting_started/quickstart.md delete mode 100644 docs/source/getting_started/quickstart.rst create mode 100644 docs/source/index.md delete mode 100644 docs/source/index.rst create mode 100644 docs/source/models/detection.md create mode 100644 docs/source/models/end_to_end.md create mode 100644 docs/source/models/index.md create mode 100644 docs/source/models/layout.md create mode 100644 docs/source/models/recognition.md create mode 100644 docs/source/models/table_structure.md create mode 100644 docs/source/models/vocabs.md delete mode 100644 docs/source/modules/contrib.rst delete mode 100644 docs/source/modules/datasets.rst delete mode 100644 docs/source/modules/io.rst delete mode 100644 docs/source/modules/transforms.rst delete mode 100644 docs/source/modules/utils.rst delete mode 120000 docs/source/notebooks.rst create mode 100644 docs/source/training/custom_models.md create mode 100644 docs/source/training/datasets.md create mode 100644 docs/source/training/index.md create mode 100644 docs/source/training/sharing_models.md create mode 100644 docs/source/training/train.md create mode 100644 docs/source/user_guide/artefacts.md create mode 100644 docs/source/user_guide/cli.md create mode 100644 docs/source/user_guide/exporting.md create mode 100644 docs/source/user_guide/faq.md create mode 100644 docs/source/user_guide/index.md create mode 100644 docs/source/user_guide/kie.md create mode 100644 docs/source/user_guide/layout_and_tables.md create mode 100644 docs/source/user_guide/loading_documents.md create mode 100644 docs/source/user_guide/ocr_predictor.md create mode 100644 docs/source/user_guide/results.md create mode 100644 docs/source/user_guide/rotated_documents.md create mode 100644 docs/source/user_guide/standalone_predictors.md delete mode 100644 docs/source/using_doctr/custom_models_training.rst delete mode 100644 docs/source/using_doctr/running_on_aws.rst delete mode 100644 docs/source/using_doctr/sharing_models.rst delete mode 100644 docs/source/using_doctr/using_cli.rst delete mode 100644 docs/source/using_doctr/using_contrib_modules.rst delete mode 100644 docs/source/using_doctr/using_datasets.rst delete mode 100644 docs/source/using_doctr/using_model_export.rst delete mode 100644 docs/source/using_doctr/using_models.rst diff --git a/.codacy.yml b/.codacy.yml index 626bab79e8..783221dcea 100644 --- a/.codacy.yml +++ b/.codacy.yml @@ -1,7 +1,7 @@ --- # Codacy configuration file # https://docs.codacy.com/repositories-configure/codacy-configuration-file/ -# Analyze only doctr/ and references/ — everything else is excluded. +# Analyze only doctr/ and references/ - everything else is excluded. exclude_paths: - ".github/**" diff --git a/.gitignore b/.gitignore index b2169a9bc9..5ed675fbf6 100644 --- a/.gitignore +++ b/.gitignore @@ -70,6 +70,8 @@ instance/ # Sphinx documentation docs/_build/ +docs/build/ +docs/source/models/_generated/ # PyBuilder target/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 1b702b4795..907ec7eb72 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -16,7 +16,7 @@ repos: - id: no-commit-to-branch args: ['--branch', 'main'] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.17.1 + rev: v0.17.3 hooks: - id: ruff args: [ --fix ] diff --git a/README.md b/README.md index 561e637437..ee52fdb638 100644 --- a/README.md +++ b/README.md @@ -25,7 +25,7 @@ What you can expect from this repository: ### Getting your pretrained model End-to-End OCR is achieved in docTR using a two-stage approach: text detection (localizing words), then text recognition (identify all characters in the word). -As such, you can select the architecture used for [text detection](https://mindee.github.io/doctr/latest/modules/models.html#doctr-models-detection), and the one for [text recognition](https://mindee.github.io/doctr/latest//modules/models.html#doctr-models-recognition) from the list of available implementations. +As such, you can select the architecture used for [text detection](https://mindee.github.io/doctr/latest/models/detection.html), and the one for [text recognition](https://mindee.github.io/doctr/latest/models/recognition.html) from the list of available implementations. ```python from doctr.models import ocr_predictor @@ -119,7 +119,7 @@ plt.show() ![Synthesis sample](https://github.com/mindee/doctr/raw/main/docs/images/synthesized_sample.png) The `ocr_predictor` returns a `Document` object with a nested structure (with `Page`, `Block`, `Line`, `Word`, `Artefact`). -To get a better understanding of our document model, check our [documentation](https://mindee.github.io/doctr/modules/io.html#document-structure): +To get a better understanding of our document model, check our [documentation](https://mindee.github.io/doctr/latest/getting_started/concepts.html#the-document-tree): You can also export them as a nested dict, more appropriate for JSON format: diff --git a/docs/README.md b/docs/README.md index 0aabfe1048..3ac55a93f2 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,13 +1,85 @@ -# Contribute to Documentation +# docTR documentation -Please have a look at our [contribution guide](../CONTRIBUTING.md) to see how to install -the development environment and how to generate the documentation. +The source of [mindee.github.io/doctr](https://mindee.github.io/doctr), built with [Sphinx](https://www.sphinx-doc.org), +the [PyData theme](https://pydata-sphinx-theme.readthedocs.io), [MyST Markdown](https://myst-parser.readthedocs.io) +and [sphinx-design](https://sphinx-design.readthedocs.io). -To install only the `docs` environment, you can do: +## Build it locally ```bash -# Make sure you are at the root of the repository before executing these commands -python -m pip install --upgrade pip -pip install -e .[viz,html] -pip install -e .[docs] +# From the root of the repository +pip install -e ".[viz,html,docs]" +make docs-single-version ``` + +Then open `docs/_build/index.html` in your browser. Sphinx only rebuilds the pages that changed: delete +`docs/_build` to force a full rebuild. For a live preview while you write, `pip install sphinx-autobuild` and run +`sphinx-autobuild docs/source docs/_build`. + +`make docs` builds all the deployed versions (see `build.sh`), as the CI does. + +## Structure + +| Folder | Section | Content | +| --- | --- | --- | +| `source/index.md` | Landing page | Hero, features and entry points | +| `source/getting_started/` | Get started | Installation, quickstart, core concepts | +| `source/user_guide/` | User guide | One page per feature, task-oriented | +| `source/models/` | Model zoo | Architectures, benchmarks and vocabularies | +| `source/training/` | Training | Datasets, training scripts, custom and shared models | +| `source/deployment/` | Deployment | Performance, ONNX, Docker, web API, configuration | +| `source/api/` | API reference | Generated from the docstrings with `autodoc` | +| `source/community/` | Community | Help, tools, resources, contributing and changelog | +| `source/_static/` | | CSS (`css/doctr.css`), JS, fonts, images and the version switcher (`switcher.json`) | +| `source/_templates/` | | Footer components | +| `source/_includes/` | | Snippets included in several pages (pipeline diagram) | + +Each section has an `index.md` with an overview and the `toctree` of its pages. The top navigation bar lists the +sections of the `toctree` of `source/index.md`. + +## Writing pages + +Pages are written in [MyST Markdown](https://myst-parser.readthedocs.io/en/latest/syntax/typography.html), a superset +of CommonMark that supports all Sphinx features: + +````markdown +# Page title + +A first paragraph that says what the page is about: it is displayed as a lead. + +## Section + +Link to another page: [](../user_guide/exporting.md), or to a section: [](exporting.md#figures). +Link to the API: {func}`~doctr.models.ocr_predictor`, {class}`~doctr.io.Document`. + +```python +from doctr.models import ocr_predictor +``` + +:::{tip} +Admonitions: note, tip, important, warning, seealso, dropdown... +::: + +::::{tab-set} +:::{tab-item} pip +... +::: +:::: +```` + +Guidelines: + +- **Show, then explain**: start sections with a short runnable snippet, then describe the options. +- **Keep snippets runnable**: complete imports, realistic file names, and real outputs. The outputs shown in the + guides come from `source/_static/images/examples/invoice.pdf`. +- **One topic per page**, and link to the related pages instead of repeating them. +- **Cards** (`grid-item-card`) are for navigation, **tabs** for alternatives (e.g. CPU / GPU), **dropdowns** for + details most readers can skip. +- Document new public functions and classes in the docstrings (Google style), and add them to the matching page of + `source/api/`. + +## Releasing a new version + +1. Add the release to `source/changelog.md`. +2. Add it to `source/_static/switcher.json` and move the `(stable)` label and `"preferred": true` to it. +3. In `build.sh`, build the previous stable release into its own folder and the new one at the root. diff --git a/docs/build.sh b/docs/build.sh index 29e74cc8a5..c8234350f3 100644 --- a/docs/build.sh +++ b/docs/build.sh @@ -1,40 +1,56 @@ +# Build the multi-version documentation deployed on https://mindee.github.io/doctr +# +# build/ -> latest stable release +# build/latest/ -> main branch +# build/vX.Y.Z/ -> previous releases +# +# Every version is rendered with the configuration, theme and static files of the main branch, so that they all share +# the same look and version switcher (`source/_static/switcher.json`, to update at each release). +# +# deploy_doc +# commit: commit to build ("" for the current one) +# folder: output folder in build/ ("latest", "vX.Y.Z", or "" for the stable version at the root) +# version: version shown as selected in the version switcher (must match an entry of switcher.json) function deploy_doc(){ - if [ ! -z "$1" ] - then - git checkout $1 + local commit=$1 folder=$2 version=$3 + if [ -n "$commit" ]; then + git checkout "$commit" fi - COMMIT=$(git rev-parse --short HEAD) - echo "Creating doc at commit" $COMMIT "and pushing to folder $2" + echo "Creating doc at commit $(git rev-parse --short HEAD) (version: $version)" pip install -U .. - if [ ! -z "$2" ] - then - if [ "$2" == "latest" ]; then - echo "Pushing main" - sphinx-build source _build -a && mkdir build && mkdir build/$2 && cp -a _build/* build/$2/ - elif [ -d build/$2 ]; then - echo "Directory" $2 "already exists" - else - echo "Pushing version" $2 - cp -r _static source/ && cp _conf.py source/conf.py - sphinx-build source _build -a - mkdir build/$2 && cp -a _build/* build/$2/ && git checkout source/ && git clean -f source/ - fi + + # Older commits are rendered with the current configuration and assets + local legacy=0 + if [ -n "$commit" ]; then + legacy=$([ -f source/index.md ] && echo 0 || echo 1) + rm -rf source/_static source/_templates + cp -r _static _templates source/ && cp _conf.py source/conf.py + fi + + local out=build + if [ -n "$folder" ]; then + out=build/$folder + fi + if [ -d "$out" ] && [ -n "$folder" ]; then + echo "Directory $out already exists" else - echo "Pushing stable" - cp -r _static source/ && cp _conf.py source/conf.py - sphinx-build source build -a && git checkout source/ && git clean -f source/ + DOCTR_DOCS_VERSION=$version DOCTR_DOCS_LEGACY=$legacy sphinx-build source _build -a + mkdir -p "$out" && cp -a _build/. "$out/" && rm -rf _build + fi + + if [ -n "$commit" ]; then + git checkout source/ && git clean -fd source/ fi } # You can find the commit for each tag on https://github.com/mindee/doctr/tags if [ -d build ]; then rm -Rf build; fi -cp -r source/_static . -cp source/conf.py _conf.py +cp -r source/_static source/_templates . && cp source/conf.py _conf.py git fetch --all --tags --unshallow -deploy_doc "" latest -deploy_doc "1c9ce92" v0.11.0 -deploy_doc "97d4006" v0.12.0 -deploy_doc "7dabbe1" v1.0.0 -deploy_doc "6541a6e" v1.0.1 -deploy_doc "2d8e244" # v1.1.0 Latest stable release -rm -rf _build _static _conf.py +deploy_doc "" latest latest +deploy_doc "1c9ce92" v0.11.0 v0.11.0 +deploy_doc "97d4006" v0.12.0 v0.12.0 +deploy_doc "7dabbe1" v1.0.0 v1.0.0 +deploy_doc "6541a6e" v1.0.1 v1.0.1 +deploy_doc "2d8e244" "" v1.1.0 # Latest stable release +rm -rf _build _static _templates _conf.py diff --git a/docs/source/_includes/pipeline.html b/docs/source/_includes/pipeline.html new file mode 100644 index 0000000000..65c0a06a5b --- /dev/null +++ b/docs/source/_includes/pipeline.html @@ -0,0 +1,67 @@ + diff --git a/docs/source/_static/css/doctr.css b/docs/source/_static/css/doctr.css new file mode 100644 index 0000000000..6a8e0906b7 --- /dev/null +++ b/docs/source/_static/css/doctr.css @@ -0,0 +1,1022 @@ +/* ========================================================================== + docTR documentation theme (on top of pydata-sphinx-theme) + ========================================================================== */ + +/* -- Self-hosted fonts (SIL Open Font License, see ../fonts) ---------------- */ + +@font-face { + font-family: "Inter"; + font-style: normal; + font-display: swap; + font-weight: 100 900; + src: url("../fonts/inter-latin-wght-normal.woff2") format("woff2-variations"); + unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, + U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; +} + +@font-face { + font-family: "Inter"; + font-style: normal; + font-display: swap; + font-weight: 100 900; + src: url("../fonts/inter-latin-ext-wght-normal.woff2") format("woff2-variations"); + unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, + U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF; +} + +@font-face { + font-family: "JetBrains Mono"; + font-style: normal; + font-display: swap; + font-weight: 100 800; + src: url("../fonts/jetbrains-mono-latin-wght-normal.woff2") format("woff2-variations"); + unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, + U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; +} + +@font-face { + font-family: "JetBrains Mono"; + font-style: normal; + font-display: swap; + font-weight: 100 800; + src: url("../fonts/jetbrains-mono-latin-ext-wght-normal.woff2") format("woff2-variations"); + unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, + U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF; +} + +/* -- Design tokens ---------------------------------------------------------- */ + +:root { + --doctr-navy: #001e3f; + --doctr-navy-2: #0a2f5c; + --doctr-red: #ff003e; + --doctr-red-soft: #fd3246; + --doctr-radius: 12px; + --doctr-radius-sm: 8px; + + --pst-font-family-base: "Inter", ui-sans-serif, system-ui, -apple-system, "Segoe UI", Roboto, "Helvetica Neue", + Arial, sans-serif; + --pst-font-family-heading: var(--pst-font-family-base); + --pst-font-family-monospace: "JetBrains Mono", ui-monospace, SFMono-Regular, Menlo, Consolas, monospace; + --pst-font-size-base: 1rem; + --pst-header-height: 4rem; + --pst-sidebar-font-size: 0.92rem; +} + +html[data-theme="light"] { + --pst-color-primary: #0b57d0; + --pst-color-primary-highlight: #0842a0; + --pst-color-primary-bg: #e7efff; + --pst-color-secondary: #e0003a; + --pst-color-secondary-highlight: #b8002f; + --pst-color-secondary-bg: #ffe4ea; + --pst-color-accent: #e0003a; + --pst-color-link: var(--pst-color-primary); + --pst-color-link-hover: var(--pst-color-primary-highlight); + --pst-color-text-base: #1b2433; + --pst-color-text-muted: #556274; + --pst-color-heading: #0b1b33; + --pst-color-background: #ffffff; + --pst-color-on-background: #ffffff; + --pst-color-surface: #f5f7fa; + --pst-color-border: #e1e6ed; + --pst-color-border-muted: #eceff4; + --pst-color-inline-code: #1f2a3d; + --pst-color-target: #fff3c4; + --pst-color-table-row-hover-bg: #eef3ff; + --sd-color-primary: var(--pst-color-primary); + --sd-color-primary-highlight: var(--pst-color-primary-highlight); + --sd-color-primary-bg: var(--pst-color-primary-bg); + --sd-color-secondary: var(--pst-color-secondary); + --sd-color-secondary-highlight: var(--pst-color-secondary-highlight); + --sd-color-secondary-bg: var(--pst-color-secondary-bg); + --sd-color-card-border: var(--pst-color-border); + + --doctr-code-bg: #f6f8fa; + --doctr-card-shadow: 0 1px 2px rgba(16, 24, 40, 0.04), 0 1px 3px rgba(16, 24, 40, 0.06); + --doctr-card-shadow-hover: 0 10px 24px -8px rgba(11, 87, 208, 0.25); +} + +html[data-theme="dark"] { + --pst-color-primary: #7cacf8; + --pst-color-primary-highlight: #a8c7fa; + --pst-color-primary-bg: #0d2a55; + --pst-color-secondary: #ff5c7a; + --pst-color-secondary-highlight: #ff8aa0; + --pst-color-secondary-bg: #4a0f1f; + --pst-color-accent: #ff5c7a; + --pst-color-link: var(--pst-color-primary); + --pst-color-link-hover: var(--pst-color-primary-highlight); + --pst-color-text-base: #d5dce6; + --pst-color-text-muted: #98a3b3; + --pst-color-heading: #f1f5fa; + --pst-color-background: #0b111c; + --pst-color-on-background: #111a28; + --pst-color-surface: #152033; + --pst-color-border: #243247; + --pst-color-border-muted: #1b2639; + --pst-color-inline-code: #e3e9f2; + --pst-color-target: #4a3f05; + --pst-color-table-row-hover-bg: #17284a; + --sd-color-primary: var(--pst-color-primary); + --sd-color-primary-highlight: var(--pst-color-primary-highlight); + --sd-color-primary-bg: var(--pst-color-primary-bg); + --sd-color-primary-text: #0b111c; + --sd-color-secondary: var(--pst-color-secondary); + --sd-color-secondary-highlight: var(--pst-color-secondary-highlight); + --sd-color-secondary-bg: var(--pst-color-secondary-bg); + --sd-color-secondary-text: #0b111c; + --sd-color-card-border: var(--pst-color-border); + --sd-color-card-background: var(--pst-color-on-background); + + --doctr-code-bg: #0f1726; + --doctr-card-shadow: 0 1px 2px rgba(0, 0, 0, 0.3); + --doctr-card-shadow-hover: 0 12px 28px -10px rgba(124, 172, 248, 0.35); +} + +/* -- Typography ------------------------------------------------------------- */ + +body { + font-feature-settings: "cv11", "ss01"; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +.bd-article h1, +.bd-article h2, +.bd-article h3, +.bd-article h4 { + font-weight: 700; + letter-spacing: -0.018em; +} + +.bd-article h1 { + font-size: 2.3rem; + letter-spacing: -0.03em; + margin-bottom: 0.6rem; +} + +.bd-article h2 { + font-size: 1.6rem; + margin-top: 2.6rem; + padding-top: 0.2rem; +} + +.bd-article h3 { + font-size: 1.22rem; + margin-top: 2rem; +} + +/* Lead paragraph: the first paragraph right under the page title */ +.bd-article section > h1 + p { + font-size: 1.12rem; + line-height: 1.65; + color: var(--pst-color-text-muted); +} + +.bd-article p, +.bd-article li { + line-height: 1.7; +} + +a { + text-underline-offset: 0.18em; +} + +/* -- Header ------------------------------------------------------------------ */ + +.bd-header { + box-shadow: none; + border-bottom: 1px solid var(--pst-color-border); + backdrop-filter: saturate(160%) blur(10px); + -webkit-backdrop-filter: saturate(160%) blur(10px); +} + +html[data-theme="light"] .bd-header { + background-color: rgba(255, 255, 255, 0.86) !important; +} + +html[data-theme="dark"] .bd-header { + background-color: rgba(11, 17, 28, 0.86) !important; +} + +.navbar-brand img { + height: 30px; +} + +.bd-header .navbar-header-items__start .navbar-item { + gap: 0.75rem; +} + +.bd-header ul.navbar-nav > li.nav-item > .nav-link { + font-weight: 550; + font-size: 0.95rem; +} + +.bd-header ul.navbar-nav > li.nav-item.current > .nav-link { + color: var(--pst-color-heading); +} + +.bd-header ul.navbar-nav > li.nav-item.current > .nav-link::before { + border-bottom: 3px solid var(--pst-color-secondary); +} + +/* Version switcher button */ +.version-switcher__button { + font-size: 0.82rem; + font-weight: 600; + border-radius: 999px; + padding: 0.15rem 0.7rem; + border-color: var(--pst-color-border); +} + +/* -- Sidebars ---------------------------------------------------------------- */ + +.bd-sidebar-primary { + border-right: 1px solid var(--pst-color-border-muted); +} + +.bd-sidebar-primary .sidebar-primary-items__start .bd-links__title { + font-size: 0.78rem; + text-transform: uppercase; + letter-spacing: 0.08em; + color: var(--pst-color-text-muted); +} + +.bd-sidebar-primary .toctree-l1 > a, +.bd-sidebar-primary .toctree-l2 > a { + border-radius: var(--doctr-radius-sm); +} + +nav.bd-links li > a:hover { + background-color: var(--pst-color-surface); + text-decoration: none; +} + +nav.bd-links .current > a { + box-shadow: inset 3px 0 0 var(--pst-color-secondary); + background-color: var(--pst-color-surface); + font-weight: 600; +} + +.bd-sidebar-secondary { + font-size: 0.88rem; +} + +/* -- Code -------------------------------------------------------------------- */ + +div.highlight, +div.highlight pre { + background-color: var(--doctr-code-bg) !important; +} + +div.highlight { + border: 1px solid var(--pst-color-border); + border-radius: var(--doctr-radius-sm); +} + +/* Code must read exactly as typed: no ligatures (e.g. "==" or "->") */ +pre, +code, +kbd, +.doctr-tree { + font-variant-ligatures: none; + font-feature-settings: "liga" 0, "calt" 0; +} + +div.highlight pre { + border: none; + border-radius: var(--doctr-radius-sm); + font-size: 0.86rem; + line-height: 1.6; + padding: 1rem 1.1rem; + box-shadow: none; +} + +.bd-content div.literal-block-wrapper, +div[class*="highlight-"] { + border-radius: var(--doctr-radius-sm); +} + +code.literal { + font-size: 0.86em; + padding: 0.12em 0.36em; + border-radius: 6px; + border: 1px solid var(--pst-color-border-muted); + background-color: var(--pst-color-surface); +} + +/* Code-block captions (file names) */ +div.code-block-caption { + font-family: var(--pst-font-family-monospace); + font-size: 0.8rem; + padding: 0.45rem 0.9rem; + border: 1px solid var(--pst-color-border); + border-bottom: none; + border-radius: var(--doctr-radius-sm) var(--doctr-radius-sm) 0 0; + background: var(--pst-color-surface); +} + +div.code-block-caption + div.highlight-default, +div.code-block-caption + div[class*="highlight-"] div.highlight { + border-top-left-radius: 0; + border-top-right-radius: 0; +} + +/* Keep the page TOC and the navigation light */ +.bd-toc code.literal, +nav.bd-links code.literal, +.bd-breadcrumbs code.literal { + border: none; + background: none; + padding: 0; + font-size: 0.92em; +} + +/* Inline code inside links keeps the link color */ +a code.literal, +a:visited code.literal { + color: var(--pst-color-link); +} + +/* Figures */ +.bd-article img.align-center { + border: 1px solid var(--pst-color-border); + border-radius: var(--doctr-radius); + box-shadow: var(--doctr-card-shadow); + margin: 1.25rem auto 1.75rem; +} + +button.copybtn { + border-radius: 6px; +} + +/* -- Admonitions ------------------------------------------------------------ */ + +.admonition, +div.admonition { + border-radius: var(--doctr-radius-sm); + box-shadow: none; + border: 1px solid var(--pst-color-border); + border-left-width: 4px; +} + +/* -- Tables ------------------------------------------------------------------ */ + +.bd-article table.table { + font-size: 0.9rem; + border-radius: var(--doctr-radius-sm); +} + +.bd-article table.table th { + font-weight: 600; + white-space: nowrap; +} + +.bd-article table.table td code.literal { + white-space: nowrap; +} + +/* -- sphinx-design ------------------------------------------------------------ */ + +.sd-card { + border-radius: var(--doctr-radius); + box-shadow: var(--doctr-card-shadow); + transition: transform 0.15s ease, box-shadow 0.15s ease, border-color 0.15s ease; +} + +.sd-card.sd-card-hover:hover { + transform: translateY(-2px); + box-shadow: var(--doctr-card-shadow-hover); + border-color: var(--pst-color-primary); +} + +.sd-card .sd-card-title { + font-weight: 650; + font-size: 1.02rem; + margin-bottom: 0.35rem; +} + +.sd-card .sd-card-body p { + color: var(--pst-color-text-muted); + font-size: 0.93rem; + line-height: 1.55; +} + +.sd-card .sd-card-title .sd-octicon, +.sd-card .sd-card-title svg { + color: var(--pst-color-secondary); + margin-right: 0.35rem; +} + +.sd-tab-set > label { + font-weight: 600; + font-size: 0.9rem; +} + +.sd-tab-set > input:checked + label { + border-color: var(--pst-color-secondary); + color: var(--pst-color-heading); +} + +.sd-badge { + font-weight: 600; + letter-spacing: 0.01em; +} + +/* Step-by-step lists (".steps" class on ordered lists) */ +ol.steps { + counter-reset: step; + list-style: none; + padding-left: 0; +} + +ol.steps > li { + counter-increment: step; + position: relative; + padding-left: 2.6rem; + margin-bottom: 1rem; +} + +ol.steps > li::before { + content: counter(step); + position: absolute; + left: 0; + top: 0.05rem; + width: 1.75rem; + height: 1.75rem; + border-radius: 999px; + display: grid; + place-items: center; + font-size: 0.85rem; + font-weight: 700; + color: #fff; + background: var(--doctr-navy); +} + +html[data-theme="dark"] ol.steps > li::before { + background: var(--pst-color-primary-bg); + color: var(--pst-color-primary); +} + +/* -- API reference ------------------------------------------------------------ */ + +dl.py > dt { + font-family: var(--pst-font-family-monospace); + font-size: 0.88rem; + background: var(--pst-color-surface); + border: 1px solid var(--pst-color-border); + border-left: 3px solid var(--pst-color-primary); + border-radius: 6px; + padding: 0.5rem 0.75rem; +} + +dl.py.method > dt, +dl.py.attribute > dt, +dl.py.property > dt { + border-left-color: var(--pst-color-border); + background: transparent; +} + +dl.py > dd { + margin-left: 1.25rem; +} + +dl.field-list > dt { + font-weight: 600; +} + +/* -- Pipeline diagram ---------------------------------------------------------- */ + +.doctr-diagram { + margin: 1.5rem 0 2rem; + padding: 1.25rem; + border: 1px solid var(--pst-color-border); + border-radius: var(--doctr-radius); + background: var(--pst-color-surface); + overflow-x: auto; +} + +.doctr-diagram svg { + display: block; + width: 100%; + min-width: 640px; + height: auto; +} + +.doctr-diagram .box { + fill: var(--pst-color-background); + stroke: var(--pst-color-border); + stroke-width: 1.5; +} + +.doctr-diagram .box.core { + stroke: var(--pst-color-primary); +} + +.doctr-diagram .box.opt { + stroke-dasharray: 5 4; +} + +.doctr-diagram .box.out { + fill: var(--doctr-navy); + stroke: var(--doctr-navy); +} + +.doctr-diagram .title { + font: 600 14px var(--pst-font-family-base); + fill: var(--pst-color-heading); +} + +.doctr-diagram .sub { + font: 400 11.5px var(--pst-font-family-base); + fill: var(--pst-color-text-muted); +} + +.doctr-diagram .mono { + font: 500 11px var(--pst-font-family-monospace); + fill: var(--pst-color-primary); +} + +.doctr-diagram .out .title, +.doctr-diagram .title.on-dark { + fill: #ffffff; +} + +.doctr-diagram .sub.on-dark { + fill: #b8c7dc; +} + +.doctr-diagram .arrow { + stroke: var(--pst-color-text-muted); + stroke-width: 1.5; + fill: none; +} + +.doctr-diagram .arrow-head { + fill: var(--pst-color-text-muted); +} + +.doctr-diagram .legend { + font: 400 11px var(--pst-font-family-base); + fill: var(--pst-color-text-muted); +} + +/* -- Document tree (concepts page) ------------------------------------------------ */ + +.doctr-tree { + font-family: var(--pst-font-family-monospace); + font-size: 0.86rem; + line-height: 1.75; + padding: 1rem 1.25rem; + border: 1px solid var(--pst-color-border); + border-radius: var(--doctr-radius-sm); + background: var(--doctr-code-bg); + white-space: pre; + overflow-x: auto; + margin-bottom: 1.5rem; +} + +.doctr-tree .k { + color: var(--pst-color-primary); + font-weight: 600; +} + +.doctr-tree .c { + color: var(--pst-color-text-muted); +} + +/* ========================================================================== + Landing page + ========================================================================== */ + +/* Full-width landing page: hide the article chrome */ +body:has(.doctr-hero) .bd-main .bd-content .bd-article-container { + max-width: 100%; +} + +body:has(.doctr-hero) .bd-header-article, +body:has(.doctr-hero) .prev-next-area, +body:has(.doctr-hero) .bd-sidebar-secondary { + display: none !important; +} + +body:has(.doctr-hero) .bd-main .bd-content .bd-article-container .bd-article { + padding-top: 0; +} + +body:has(.doctr-hero) .bd-container__inner { + max-width: 1240px; +} + +body:has(.doctr-hero) .bd-article > section > h1:first-child { + /* The page title is rendered by the hero: keep it for screen readers only */ + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border: 0; +} + +.doctr-hero { + position: relative; + overflow: hidden; + margin: 1.25rem 0 3rem; + padding: 3.5rem 3rem 3.25rem; + border-radius: 20px; + color: #e8eef7; + background: + radial-gradient(900px 380px at 100% 0%, rgba(255, 0, 62, 0.28), transparent 60%), + radial-gradient(700px 400px at 0% 100%, rgba(11, 87, 208, 0.35), transparent 60%), + linear-gradient(135deg, #001e3f 0%, #04254d 55%, #0a2f5c 100%); +} + +/* Pixel grid echoing the docTR mark */ +.doctr-hero::before { + content: ""; + position: absolute; + inset: 0; + background-image: + linear-gradient(rgba(255, 255, 255, 0.05) 1px, transparent 1px), + linear-gradient(90deg, rgba(255, 255, 255, 0.05) 1px, transparent 1px); + background-size: 28px 28px; + mask-image: linear-gradient(120deg, transparent 20%, #000 70%); + -webkit-mask-image: linear-gradient(120deg, transparent 20%, #000 70%); + pointer-events: none; +} + +.doctr-hero__grid { + position: relative; + display: grid; + grid-template-columns: minmax(0, 1.05fr) minmax(0, 1fr); + gap: 2.5rem; + align-items: center; +} + +.doctr-hero__eyebrow { + display: inline-flex; + align-items: center; + gap: 0.5rem; + font-size: 0.8rem; + font-weight: 600; + letter-spacing: 0.06em; + text-transform: uppercase; + color: #ffd2dc; + background: rgba(255, 0, 62, 0.16); + border: 1px solid rgba(255, 92, 122, 0.35); + padding: 0.3rem 0.75rem; + border-radius: 999px; + margin-bottom: 1.1rem; +} + +.doctr-hero p.doctr-hero__title { + font-size: clamp(2.2rem, 4.2vw, 3.3rem); + line-height: 1.08; + font-weight: 800; + letter-spacing: -0.035em; + color: #ffffff; + margin: 0 0 1rem; +} + +.doctr-hero__title span { + background: linear-gradient(90deg, #ff4d6d, #ff8fa3); + -webkit-background-clip: text; + background-clip: text; + color: transparent; +} + +.doctr-hero p.doctr-hero__lead { + font-size: 1.12rem; + line-height: 1.65; + color: #c4d1e3; + max-width: 34rem; + margin-bottom: 1.6rem; +} + +.doctr-hero__actions { + display: flex; + flex-wrap: wrap; + gap: 0.75rem; + margin-bottom: 1.5rem; +} + +.doctr-btn { + display: inline-flex; + align-items: center; + gap: 0.5rem; + font-weight: 650; + font-size: 0.97rem; + padding: 0.7rem 1.2rem; + border-radius: 10px; + text-decoration: none !important; + transition: transform 0.15s ease, background-color 0.15s ease, border-color 0.15s ease; +} + +.doctr-btn:hover { + transform: translateY(-1px); +} + +.doctr-btn--primary { + color: #ffffff !important; + background: var(--doctr-red); + box-shadow: 0 8px 22px -8px rgba(255, 0, 62, 0.65); +} + +.doctr-btn--primary:hover { + background: #e60038; +} + +.doctr-btn--ghost { + color: #ffffff !important; + background: rgba(255, 255, 255, 0.06); + border: 1px solid rgba(255, 255, 255, 0.22); +} + +.doctr-btn--ghost:hover { + background: rgba(255, 255, 255, 0.12); + border-color: rgba(255, 255, 255, 0.4); +} + +.doctr-hero__install { + display: inline-flex; + align-items: center; + gap: 0.75rem; + font-family: var(--pst-font-family-monospace); + font-size: 0.9rem; + color: #e8eef7; + background: rgba(0, 0, 0, 0.28); + border: 1px solid rgba(255, 255, 255, 0.12); + border-radius: 10px; + padding: 0.55rem 0.6rem 0.55rem 0.9rem; +} + +.doctr-hero__install .prompt { + color: #ff8fa3; + user-select: none; +} + +.doctr-hero__install button { + font: inherit; + font-size: 0.78rem; + font-family: var(--pst-font-family-base); + font-weight: 600; + color: #c4d1e3; + background: rgba(255, 255, 255, 0.08); + border: 1px solid rgba(255, 255, 255, 0.14); + border-radius: 6px; + padding: 0.15rem 0.55rem; + cursor: pointer; +} + +.doctr-hero__install button:hover { + color: #ffffff; + background: rgba(255, 255, 255, 0.16); +} + +/* Code window in the hero */ +.doctr-window { + border-radius: 14px; + background: #0b1424; + border: 1px solid rgba(255, 255, 255, 0.1); + box-shadow: 0 30px 60px -20px rgba(0, 0, 0, 0.6); + overflow: hidden; +} + +.doctr-window__bar { + display: flex; + align-items: center; + gap: 0.4rem; + padding: 0.7rem 0.9rem; + border-bottom: 1px solid rgba(255, 255, 255, 0.07); +} + +.doctr-window__bar i { + width: 11px; + height: 11px; + border-radius: 50%; + background: #33415a; +} + +.doctr-window__bar i:first-child { + background: #ff5f57; +} + +.doctr-window__bar i:nth-child(2) { + background: #febc2e; +} + +.doctr-window__bar i:nth-child(3) { + background: #28c840; +} + +.doctr-window__bar span { + margin-left: 0.6rem; + font-family: var(--pst-font-family-monospace); + font-size: 0.76rem; + color: #7f8ea6; +} + +.doctr-window pre { + margin: 0; + padding: 1.1rem 1.25rem 1.3rem; + font-family: var(--pst-font-family-monospace); + font-size: 0.86rem; + line-height: 1.75; + color: #d6deeb; + background: transparent; + border: none; + white-space: pre; + overflow-x: auto; +} + +.doctr-window .tk-k { + color: #ff7a93; +} + +.doctr-window .tk-f { + color: #82aaff; +} + +.doctr-window .tk-s { + color: #a5e075; +} + +.doctr-window .tk-c { + color: #637392; + font-style: italic; +} + +.doctr-window .tk-n { + color: #f9c859; +} + +.doctr-window .tk-o { + color: #9fb0c8; +} + +.doctr-window__output { + border-top: 1px dashed rgba(255, 255, 255, 0.1); + padding: 0.85rem 1.25rem 1rem; + font-family: var(--pst-font-family-monospace); + font-size: 0.8rem; + line-height: 1.6; + color: #8fa3bf; + white-space: pre; + overflow-x: auto; +} + +.doctr-window__output b { + color: #e8eef7; + font-weight: 500; +} + +/* Stats strip */ +.doctr-stats { + position: relative; + display: flex; + flex-wrap: wrap; + gap: 2.25rem; + margin-top: 2.5rem; + padding-top: 1.6rem; + border-top: 1px solid rgba(255, 255, 255, 0.1); +} + +.doctr-stats div { + display: flex; + flex-direction: column; +} + +.doctr-stats strong { + font-size: 1.5rem; + font-weight: 750; + color: #ffffff; + letter-spacing: -0.02em; +} + +.doctr-stats span { + font-size: 0.85rem; + color: #9fb0c8; +} + +/* Landing sections */ +.doctr-section-title { + text-align: center; + margin: 3.5rem auto 0.4rem; + font-size: 1.9rem !important; + font-weight: 750; + letter-spacing: -0.03em; +} + +.doctr-section-lead { + text-align: center; + max-width: 42rem; + margin: 0 auto 2rem; + color: var(--pst-color-text-muted); + font-size: 1.05rem; +} + +body:has(.doctr-hero) .sd-card .sd-card-title { + font-size: 1.05rem; +} + +.doctr-cta { + margin: 3.5rem 0 1rem; + padding: 2rem 2.25rem; + border-radius: 16px; + border: 1px solid var(--pst-color-border); + background: + radial-gradient(500px 200px at 100% 0%, rgba(255, 0, 62, 0.08), transparent 70%), + var(--pst-color-surface); + display: flex; + flex-wrap: wrap; + gap: 1.5rem; + align-items: center; + justify-content: space-between; +} + +.doctr-cta h3 { + margin: 0 0 0.35rem !important; + font-size: 1.25rem !important; +} + +.doctr-cta > div { + flex: 1 1 28rem; +} + +.doctr-cta p { + margin: 0; + color: var(--pst-color-text-muted); +} + +.doctr-cta .doctr-btn--primary { + white-space: nowrap; +} + +@media (max-width: 960px) { + .doctr-hero { + padding: 2.25rem 1.5rem; + } + + .doctr-hero__grid { + grid-template-columns: minmax(0, 1fr); + } + + .doctr-stats { + gap: 1.5rem; + } +} + +/* -- Footer ------------------------------------------------------------------ */ + +.bd-footer { + border-top: 1px solid var(--pst-color-border); + font-size: 0.86rem; +} + +.bd-footer .footer-items__center { + text-align: center; +} + +.bd-footer p { + margin: 0; + color: var(--pst-color-text-muted); +} + +/* -- Vocabulary tables ------------------------------------------------------- */ + +.vocab-table td:nth-child(1), +.vocab-table td:nth-child(2) { + white-space: nowrap; + vertical-align: top; +} + +.vocab-table td:nth-child(3) { + width: 100%; +} + +.vocab-table summary { + cursor: pointer; + font-family: var(--pst-font-family-monospace); + font-size: 0.85rem; +} + +.vocab-table .vocab-base { + font-family: var(--pst-font-family-base); + color: var(--pst-color-text-muted); +} + +.vocab-table .vocab-chars { + margin-top: 0.5rem; + font-family: var(--pst-font-family-monospace); + font-size: 0.85rem; + word-break: break-all; + max-height: 14rem; + overflow-y: auto; + padding: 0.5rem 0.65rem; + border-radius: 6px; + background: var(--pst-color-surface); +} diff --git a/docs/source/_static/css/mindee.css b/docs/source/_static/css/mindee.css deleted file mode 100644 index 94954e57c5..0000000000 --- a/docs/source/_static/css/mindee.css +++ /dev/null @@ -1,52 +0,0 @@ -/* Version control */ - -.version-button { - color: white; - border: none; - padding: 5px; - font-size: 15px; - cursor: pointer; -} - -.version-button:hover, .version-button:focus { - background-color: #5eb2e6; -} - -.version-dropdown { - display: none; - min-width: 160px; - overflow: auto; - font-size: 15px; -} - -.version-dropdown a { - color: white; - padding: 3px 4px; - text-decoration: none; - display: block; -} - -.version-dropdown a:hover { - background-color: #5eb2e6; -} - -.version-show { - display: block; -} - -h1 { - font-family: "Helvetica Neue", Arial, sans-serif; - /* style fix for headline that it fits into one line */ - font-size: 240%; -} - -h1, h2, h3, h4, h5, .caption-text { - font-family: "Helvetica Neue", Arial, sans-serif; -} - -/* Github button */ - -.github-repo { - display: flex; - justify-content: center; -} diff --git a/docs/source/_static/fonts/LICENSE-Inter.txt b/docs/source/_static/fonts/LICENSE-Inter.txt new file mode 100644 index 0000000000..40589daa9d --- /dev/null +++ b/docs/source/_static/fonts/LICENSE-Inter.txt @@ -0,0 +1,93 @@ +Copyright 2016 The Inter Project Authors (https://github.com/rsms/inter) Inter-Italic[opsz,wght].ttf: Copyright 2016 The Inter Project Authors (https://github.com/rsms/inter) + +This Font Software is licensed under the SIL Open Font License, Version 1.1. +This license is copied below, and is also available with a FAQ at: +http://scripts.sil.org/OFL + + +----------------------------------------------------------- +SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007 +----------------------------------------------------------- + +PREAMBLE +The goals of the Open Font License (OFL) are to stimulate worldwide +development of collaborative font projects, to support the font creation +efforts of academic and linguistic communities, and to provide a free and +open framework in which fonts may be shared and improved in partnership +with others. + +The OFL allows the licensed fonts to be used, studied, modified and +redistributed freely as long as they are not sold by themselves. The +fonts, including any derivative works, can be bundled, embedded, +redistributed and/or sold with any software provided that any reserved +names are not used by derivative works. The fonts and derivatives, +however, cannot be released under any other type of license. The +requirement for fonts to remain under this license does not apply +to any document created using the fonts or their derivatives. + +DEFINITIONS +"Font Software" refers to the set of files released by the Copyright +Holder(s) under this license and clearly marked as such. This may +include source files, build scripts and documentation. + +"Reserved Font Name" refers to any names specified as such after the +copyright statement(s). + +"Original Version" refers to the collection of Font Software components as +distributed by the Copyright Holder(s). + +"Modified Version" refers to any derivative made by adding to, deleting, +or substituting -- in part or in whole -- any of the components of the +Original Version, by changing formats or by porting the Font Software to a +new environment. + +"Author" refers to any designer, engineer, programmer, technical +writer or other person who contributed to the Font Software. + +PERMISSION & CONDITIONS +Permission is hereby granted, free of charge, to any person obtaining +a copy of the Font Software, to use, study, copy, merge, embed, modify, +redistribute, and sell modified and unmodified copies of the Font +Software, subject to the following conditions: + +1) Neither the Font Software nor any of its individual components, +in Original or Modified Versions, may be sold by itself. + +2) Original or Modified Versions of the Font Software may be bundled, +redistributed and/or sold with any software, provided that each copy +contains the above copyright notice and this license. These can be +included either as stand-alone text files, human-readable headers or +in the appropriate machine-readable metadata fields within text or +binary files as long as those fields can be easily viewed by the user. + +3) No Modified Version of the Font Software may use the Reserved Font +Name(s) unless explicit written permission is granted by the corresponding +Copyright Holder. This restriction only applies to the primary font name as +presented to the users. + +4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font +Software shall not be used to promote, endorse or advertise any +Modified Version, except to acknowledge the contribution(s) of the +Copyright Holder(s) and the Author(s) or with their explicit written +permission. + +5) The Font Software, modified or unmodified, in part or in whole, +must be distributed entirely under this license, and must not be +distributed under any other license. The requirement for fonts to +remain under this license does not apply to any document created +using the Font Software. + +TERMINATION +This license becomes null and void if any of the above conditions are +not met. + +DISCLAIMER +THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT +OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE +COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, +INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL +DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM +OTHER DEALINGS IN THE FONT SOFTWARE. diff --git a/docs/source/_static/fonts/LICENSE-JetBrainsMono.txt b/docs/source/_static/fonts/LICENSE-JetBrainsMono.txt new file mode 100644 index 0000000000..8f7ed67113 --- /dev/null +++ b/docs/source/_static/fonts/LICENSE-JetBrainsMono.txt @@ -0,0 +1,93 @@ +Copyright 2020 The JetBrains Mono Project Authors (https://github.com/JetBrains/JetBrainsMono) JetBrainsMono-Italic[wght].ttf: Copyright 2020 The JetBrains Mono Project Authors (https://github.com/JetBrains/JetBrainsMono) + +This Font Software is licensed under the SIL Open Font License, Version 1.1. +This license is copied below, and is also available with a FAQ at: +http://scripts.sil.org/OFL + + +----------------------------------------------------------- +SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007 +----------------------------------------------------------- + +PREAMBLE +The goals of the Open Font License (OFL) are to stimulate worldwide +development of collaborative font projects, to support the font creation +efforts of academic and linguistic communities, and to provide a free and +open framework in which fonts may be shared and improved in partnership +with others. + +The OFL allows the licensed fonts to be used, studied, modified and +redistributed freely as long as they are not sold by themselves. The +fonts, including any derivative works, can be bundled, embedded, +redistributed and/or sold with any software provided that any reserved +names are not used by derivative works. The fonts and derivatives, +however, cannot be released under any other type of license. The +requirement for fonts to remain under this license does not apply +to any document created using the fonts or their derivatives. + +DEFINITIONS +"Font Software" refers to the set of files released by the Copyright +Holder(s) under this license and clearly marked as such. This may +include source files, build scripts and documentation. + +"Reserved Font Name" refers to any names specified as such after the +copyright statement(s). + +"Original Version" refers to the collection of Font Software components as +distributed by the Copyright Holder(s). + +"Modified Version" refers to any derivative made by adding to, deleting, +or substituting -- in part or in whole -- any of the components of the +Original Version, by changing formats or by porting the Font Software to a +new environment. + +"Author" refers to any designer, engineer, programmer, technical +writer or other person who contributed to the Font Software. + +PERMISSION & CONDITIONS +Permission is hereby granted, free of charge, to any person obtaining +a copy of the Font Software, to use, study, copy, merge, embed, modify, +redistribute, and sell modified and unmodified copies of the Font +Software, subject to the following conditions: + +1) Neither the Font Software nor any of its individual components, +in Original or Modified Versions, may be sold by itself. + +2) Original or Modified Versions of the Font Software may be bundled, +redistributed and/or sold with any software, provided that each copy +contains the above copyright notice and this license. These can be +included either as stand-alone text files, human-readable headers or +in the appropriate machine-readable metadata fields within text or +binary files as long as those fields can be easily viewed by the user. + +3) No Modified Version of the Font Software may use the Reserved Font +Name(s) unless explicit written permission is granted by the corresponding +Copyright Holder. This restriction only applies to the primary font name as +presented to the users. + +4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font +Software shall not be used to promote, endorse or advertise any +Modified Version, except to acknowledge the contribution(s) of the +Copyright Holder(s) and the Author(s) or with their explicit written +permission. + +5) The Font Software, modified or unmodified, in part or in whole, +must be distributed entirely under this license, and must not be +distributed under any other license. The requirement for fonts to +remain under this license does not apply to any document created +using the Font Software. + +TERMINATION +This license becomes null and void if any of the above conditions are +not met. + +DISCLAIMER +THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT +OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE +COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, +INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL +DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM +OTHER DEALINGS IN THE FONT SOFTWARE. diff --git a/docs/source/_static/fonts/inter-latin-ext-wght-normal.woff2 b/docs/source/_static/fonts/inter-latin-ext-wght-normal.woff2 new file mode 100644 index 0000000000000000000000000000000000000000..479d010d2902fa3a5b48e84a973db1978b2e9949 GIT binary patch literal 85068 zcmV)ZK&!uZPew8T0RR910ZdE)6#xJL0^png0ZY&T0RR9100000000000000000000 z0000Qi(?ytiYy$36hBB-K~j|wKTTFaQeFmNKT}jeRDn(>CIF5~FKiJA3WJRdg3~Gs zjbH#Ug!=#iHUcCAn>+*{1%@sM!ZZvATWq_Mf-uH#5dD(YZ@P&Hu)S)HRb$5yj~E(b zy)Zt4PNzeRZ7DGI+wSuT2k@pVvvNDILzB8Y(f|Md|Gz1j#F$=Qy(Gy4fT99bwa&I} zw}B;9g;BU6(ovg$qwV?#xMJ{%=(=&=?4yo*^3d1DqnnP|RaA$b&TX4*+G(yEliOR0 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        + Released under the Apache 2.0 License +

        diff --git a/docs/source/_templates/maintainers.html b/docs/source/_templates/maintainers.html new file mode 100644 index 0000000000..a829b8d03b --- /dev/null +++ b/docs/source/_templates/maintainers.html @@ -0,0 +1,4 @@ +

        + Created by Mindee · developed and maintained by + t2k GmbH and the docTR community +

        diff --git a/docs/source/api/contrib.md b/docs/source/api/contrib.md new file mode 100644 index 0000000000..3b7d6009b7 --- /dev/null +++ b/docs/source/api/contrib.md @@ -0,0 +1,13 @@ +# doctr.contrib + +```{eval-rst} +.. module:: doctr.contrib +``` + +Complementary modules for document analysis pipelines, running on ONNX Runtime +(`pip install "python-doctr[contrib]"`). See [](../user_guide/artefacts.md). + +```{eval-rst} +.. autoclass:: ArtefactDetector + :members: show +``` diff --git a/docs/source/api/datasets.md b/docs/source/api/datasets.md new file mode 100644 index 0000000000..f7b0c9e827 --- /dev/null +++ b/docs/source/api/datasets.md @@ -0,0 +1,93 @@ +# doctr.datasets + +```{eval-rst} +.. module:: doctr.datasets +``` + +Public datasets, synthetic data generators and loaders for your own annotations. See [](../training/datasets.md) +for a guide. + +## Public datasets + +```{eval-rst} +.. autoclass:: FUNSD + +.. autoclass:: SROIE + +.. autoclass:: CORD + +.. autoclass:: IIIT5K + +.. autoclass:: SVT + +.. autoclass:: SVHN + +.. autoclass:: SynthText + +.. autoclass:: IC03 + +.. autoclass:: IC13 + +.. autoclass:: IMGUR5K + +.. autoclass:: MJSynth + +.. autoclass:: IIITHWS + +.. autoclass:: WILDRECEIPT + +.. autoclass:: COCOTEXT + +.. autoclass:: DocArtefacts +``` + +## Synthetic data generators + +```{eval-rst} +.. autoclass:: CharacterGenerator + +.. autoclass:: WordGenerator +``` + +## Custom datasets + +```{eval-rst} +.. autoclass:: DetectionDataset + +.. autoclass:: RecognitionDataset + +.. autoclass:: OCRDataset + +.. autoclass:: LayoutDataset + +.. autoclass:: TableStructureDataset + +.. autoclass:: OrientationDataset +``` + +## Vocabularies + +```{eval-rst} +.. data:: VOCABS + :type: dict[str, str] + + The supported vocabularies, by name. See :doc:`../models/vocabs` for the complete list. +``` + +## Utilities + +```{eval-rst} +.. autofunction:: translate + +.. autofunction:: encode_string + +.. autofunction:: decode_sequence + +.. autofunction:: encode_sequences + +.. autofunction:: pre_transform_multiclass + +.. autofunction:: crop_bboxes_from_image + +.. autofunction:: convert_target_to_relative +``` diff --git a/docs/source/api/index.md b/docs/source/api/index.md new file mode 100644 index 0000000000..393cc85a39 --- /dev/null +++ b/docs/source/api/index.md @@ -0,0 +1,61 @@ +# API reference + +The complete reference of the public API, generated from the docstrings of the source code. For a guided tour, +start with the [user guide](../user_guide/index.md). + +::::{grid} 1 2 3 3 +:gutter: 3 + +:::{grid-item-card} {octicon}`file;1.1em` doctr.io +:link: io +:link-type: doc + +Reading files, the `Document` elements, and the exporters. +::: + +:::{grid-item-card} {octicon}`cpu;1.1em` doctr.models +:link: models +:link-type: doc + +Predictors, architectures, the Hugging Face Hub integration and model utilities. +::: + +:::{grid-item-card} {octicon}`database;1.1em` doctr.datasets +:link: datasets +:link-type: doc + +Public datasets, synthetic generators, custom loaders and vocabularies. +::: + +:::{grid-item-card} {octicon}`paintbrush;1.1em` doctr.transforms +:link: transforms +:link-type: doc + +Data augmentation and pre-processing transformations. +::: + +:::{grid-item-card} {octicon}`tools;1.1em` doctr.utils +:link: utils +:link-type: doc + +Visualization, page reconstruction and evaluation metrics. +::: + +:::{grid-item-card} {octicon}`plus-circle;1.1em` doctr.contrib +:link: contrib +:link-type: doc + +Complementary modules, such as the artefact detector. +::: +:::: + +```{toctree} +:hidden: + +io +models +datasets +transforms +utils +contrib +``` diff --git a/docs/source/api/io.md b/docs/source/api/io.md new file mode 100644 index 0000000000..5957cf70c9 --- /dev/null +++ b/docs/source/api/io.md @@ -0,0 +1,159 @@ +# doctr.io + +```{eval-rst} +.. module:: doctr.io +``` + +Read documents into pages, represent the predictions as a tree of elements, and export them. + +## Reading files + +```{eval-rst} +.. autoclass:: DocumentFile + + .. automethod:: from_pdf + .. automethod:: from_images + .. automethod:: from_url + +.. autofunction:: read_pdf + +.. autofunction:: read_img_as_numpy + +.. autofunction:: read_img_as_tensor + +.. autofunction:: decode_img_as_tensor + +.. autofunction:: read_html +``` + +## Document structure + +The elements returned by the predictors, from the largest to the smallest. See +[](../getting_started/concepts.md#the-document-tree) for an overview. + +### Document + +```{eval-rst} +.. autoclass:: Document + + .. automethod:: render + .. automethod:: export + .. automethod:: export_as + .. automethod:: export_as_markdown + .. automethod:: export_as_asciidoc + .. automethod:: export_as_html + .. automethod:: export_as_xml + .. automethod:: show + .. automethod:: synthesize + .. automethod:: from_dict +``` + +### Page + +```{eval-rst} +.. autoclass:: Page + + .. automethod:: render + .. automethod:: export + .. automethod:: export_as + .. automethod:: export_as_markdown + .. automethod:: export_as_asciidoc + .. automethod:: export_as_html + .. automethod:: export_as_xml + .. automethod:: items_in_reading_order + .. automethod:: show + .. automethod:: synthesize +``` + +### Block, Line and Word + +```{eval-rst} +.. autoclass:: Block + +.. autoclass:: Line + +.. autoclass:: Word + +.. autoclass:: Artefact +``` + +### Layout and tables + +```{eval-rst} +.. autoclass:: LayoutElement + +.. autoclass:: Table + + .. automethod:: to_grid + .. automethod:: render + +.. autoclass:: TableCell + :members: row_span, col_span +``` + +### Key information extraction + +Returned by {func}`~doctr.models.kie_predictor`: predictions are grouped by class instead of by spatial layout. + +```{eval-rst} +.. autoclass:: KIEDocument + + .. automethod:: show + +.. autoclass:: KIEPage + + .. automethod:: render + .. automethod:: export + .. automethod:: export_as + .. automethod:: export_as_markdown + .. automethod:: export_as_asciidoc + .. automethod:: export_as_html + .. automethod:: export_as_xml + .. automethod:: show + +.. autoclass:: Prediction +``` + +## Exporters + +The `export_as_*` methods of the elements delegate to these exporters, which share the same reading-order +linearization. See [](../user_guide/exporting.md). + +```{eval-rst} +.. autoclass:: TextExporter + :members: export_page, export_kie_page, export_document + +.. autoclass:: MarkdownExporter + :members: export_page, export_kie_page, export_document + +.. autoclass:: AsciiDocExporter + :members: export_page, export_kie_page, export_document + +.. autoclass:: HTMLExporter + :members: export_page, export_kie_page, export_document + +.. autoclass:: XMLExporter + :members: export_page, export_kie_page, export_document + +.. autofunction:: page_reading_order +``` + +## Figures + +Rendering of the figures found by the layout model in the Markdown, AsciiDoc and HTML exports. See +[](../user_guide/exporting.md#figures). + +```{eval-rst} +.. autoclass:: FigureEncoder + :members: resolve, source, enabled, materializes + +.. autofunction:: crop_layout_region + +.. autofunction:: encode_crop + +.. autofunction:: picture_regions + +.. autofunction:: is_picture_region + +.. autofunction:: is_picture_label +``` diff --git a/docs/source/modules/models.rst b/docs/source/api/models.md similarity index 65% rename from docs/source/modules/models.rst rename to docs/source/api/models.md index 8083f9922c..00bbdf32b4 100644 --- a/docs/source/modules/models.rst +++ b/docs/source/api/models.md @@ -1,172 +1,186 @@ -doctr.models -============ +# doctr.models -.. currentmodule:: doctr.models +```{eval-rst} +.. module:: doctr.models +``` +Predictors and model architectures. All the factory functions below are also importable from `doctr.models` +directly. See the [model zoo](../models/index.md) for benchmarks. -doctr.models.classification ---------------------------- +## End-to-end predictors -.. autofunction:: doctr.models.classification.vgg16_bn_r - -.. autofunction:: doctr.models.classification.resnet18 +```{eval-rst} +.. autofunction:: doctr.models.ocr_predictor -.. autofunction:: doctr.models.classification.resnet34 +.. autofunction:: doctr.models.kie_predictor -.. autofunction:: doctr.models.classification.resnet50 +.. autoclass:: doctr.models.predictor.OCRPredictor + :members: add_hook -.. autofunction:: doctr.models.classification.resnet31 +.. autoclass:: doctr.models.kie_predictor.KIEPredictor + :members: add_hook -.. autofunction:: doctr.models.classification.mobilenet_v3_small +.. autoclass:: doctr.models.builder.DocumentBuilder -.. autofunction:: doctr.models.classification.mobilenet_v3_large +.. autoclass:: doctr.models.preprocessor.PreProcessor +``` -.. autofunction:: doctr.models.classification.mobilenet_v3_small_r +## Text detection -.. autofunction:: doctr.models.classification.mobilenet_v3_large_r +```{eval-rst} +.. autofunction:: doctr.models.detection.detection_predictor -.. autofunction:: doctr.models.classification.mobilenet_v3_small_crop_orientation +.. autofunction:: doctr.models.detection.db_resnet34 -.. autofunction:: doctr.models.classification.mobilenet_v3_small_page_orientation +.. autofunction:: doctr.models.detection.db_resnet50 -.. autofunction:: doctr.models.classification.magc_resnet31 +.. autofunction:: doctr.models.detection.db_mobilenet_v3_large -.. autofunction:: doctr.models.classification.vit_s +.. autofunction:: doctr.models.detection.linknet_resnet18 -.. autofunction:: doctr.models.classification.vit_b +.. autofunction:: doctr.models.detection.linknet_resnet34 -.. autofunction:: doctr.models.classification.textnet_tiny +.. autofunction:: doctr.models.detection.linknet_resnet50 -.. autofunction:: doctr.models.classification.textnet_small +.. autofunction:: doctr.models.detection.fast_tiny -.. autofunction:: doctr.models.classification.textnet_base +.. autofunction:: doctr.models.detection.fast_small -.. autofunction:: doctr.models.classification.vip_tiny +.. autofunction:: doctr.models.detection.fast_base +``` -.. autofunction:: doctr.models.classification.vip_base +## Text recognition -.. autofunction:: doctr.models.classification.vit_det_s +```{eval-rst} +.. autofunction:: doctr.models.recognition.recognition_predictor -.. autofunction:: doctr.models.classification.vit_det_m +.. autofunction:: doctr.models.recognition.crnn_vgg16_bn -.. autofunction:: doctr.models.classification.starnet_s3 +.. autofunction:: doctr.models.recognition.crnn_mobilenet_v3_small -.. autofunction:: doctr.models.classification.crop_orientation_predictor +.. autofunction:: doctr.models.recognition.crnn_mobilenet_v3_large -.. autofunction:: doctr.models.classification.page_orientation_predictor +.. autofunction:: doctr.models.recognition.sar_resnet31 +.. autofunction:: doctr.models.recognition.master -doctr.models.detection ----------------------- +.. autofunction:: doctr.models.recognition.vitstr_small -.. autofunction:: doctr.models.detection.linknet_resnet18 +.. autofunction:: doctr.models.recognition.vitstr_base -.. autofunction:: doctr.models.detection.linknet_resnet34 +.. autofunction:: doctr.models.recognition.parseq -.. autofunction:: doctr.models.detection.linknet_resnet50 +.. autofunction:: doctr.models.recognition.viptr_tiny +``` -.. autofunction:: doctr.models.detection.db_resnet50 +## Layout analysis -.. autofunction:: doctr.models.detection.db_mobilenet_v3_large +```{eval-rst} +.. autofunction:: doctr.models.layout.layout_predictor -.. autofunction:: doctr.models.detection.fast_tiny +.. autofunction:: doctr.models.layout.lw_detr_s -.. autofunction:: doctr.models.detection.fast_small +.. autofunction:: doctr.models.layout.lw_detr_m +``` -.. autofunction:: doctr.models.detection.fast_base +## Table structure recognition -.. autofunction:: doctr.models.detection.detection_predictor +```{eval-rst} +.. autofunction:: doctr.models.table_structure.table_predictor +.. autofunction:: doctr.models.table_structure.tablecenternet +``` -doctr.models.layout -------------------- +## Classification -.. autofunction:: doctr.models.layout.lw_detr_s +Orientation classifiers, and the backbones used by the detection and recognition models. -.. autofunction:: doctr.models.layout.lw_detr_m +```{eval-rst} +.. autofunction:: doctr.models.classification.crop_orientation_predictor -.. autofunction:: doctr.models.layout.layout_predictor +.. autofunction:: doctr.models.classification.page_orientation_predictor +.. autofunction:: doctr.models.classification.mobilenet_v3_small_crop_orientation -doctr.models.table_structure ----------------------------- +.. autofunction:: doctr.models.classification.mobilenet_v3_small_page_orientation -.. autofunction:: doctr.models.table_structure.tablecenternet +.. autofunction:: doctr.models.classification.vgg16_bn_r -.. autofunction:: doctr.models.table_structure.table_predictor +.. autofunction:: doctr.models.classification.resnet18 +.. autofunction:: doctr.models.classification.resnet31 -doctr.models.recognition ------------------------- +.. autofunction:: doctr.models.classification.resnet34 -.. autofunction:: doctr.models.recognition.crnn_vgg16_bn +.. autofunction:: doctr.models.classification.resnet50 -.. autofunction:: doctr.models.recognition.crnn_mobilenet_v3_small +.. autofunction:: doctr.models.classification.magc_resnet31 -.. autofunction:: doctr.models.recognition.crnn_mobilenet_v3_large +.. autofunction:: doctr.models.classification.mobilenet_v3_small -.. autofunction:: doctr.models.recognition.sar_resnet31 +.. autofunction:: doctr.models.classification.mobilenet_v3_large -.. autofunction:: doctr.models.recognition.master +.. autofunction:: doctr.models.classification.mobilenet_v3_small_r -.. autofunction:: doctr.models.recognition.vitstr_small +.. autofunction:: doctr.models.classification.mobilenet_v3_large_r -.. autofunction:: doctr.models.recognition.vitstr_base +.. autofunction:: doctr.models.classification.vit_s -.. autofunction:: doctr.models.recognition.parseq +.. autofunction:: doctr.models.classification.vit_b -.. autofunction:: doctr.models.recognition.viptr_tiny +.. autofunction:: doctr.models.classification.textnet_tiny -.. autofunction:: doctr.models.recognition.recognition_predictor +.. autofunction:: doctr.models.classification.textnet_small +.. autofunction:: doctr.models.classification.textnet_base -doctr.models.zoo ----------------- +.. autofunction:: doctr.models.classification.vip_tiny -.. autofunction:: doctr.models.ocr_predictor +.. autofunction:: doctr.models.classification.vip_base -.. autofunction:: doctr.models.kie_predictor +.. autofunction:: doctr.models.classification.vit_det_s +.. autofunction:: doctr.models.classification.vit_det_m -doctr.models.factory --------------------- +.. autofunction:: doctr.models.classification.starnet_s3 +``` -.. autofunction:: doctr.models.factory.login_to_hub +## Hugging Face Hub +```{eval-rst} .. autofunction:: doctr.models.factory.from_hub .. autofunction:: doctr.models.factory.push_to_hf_hub +.. autofunction:: doctr.models.factory.login_to_hub +``` -doctr.models.utils ------------------- - -.. currentmodule:: doctr.models.utils - -.. autofunction:: export_model_to_onnx +## Model utilities -.. autofunction:: add_whitelist +```{eval-rst} +.. autofunction:: doctr.models.utils.add_whitelist +.. autofunction:: doctr.models.utils.export_model_to_onnx +``` -doctr.models.reading_order --------------------------- +## Reading order -Language-independent estimation of the reading order of the elements of a page, combining a column-following -topological sort of their spatial relations with optional layout semantics (headers, footers, captions). The -reading-order-aware export of a :class:`~doctr.io.elements.Page` / :class:`~doctr.io.elements.Document` to -Markdown or AsciiDoc is exposed through the ``export_as_markdown`` / ``export_as_asciidoc`` methods of -:mod:`doctr.io.elements`. +Language-independent estimation of the reading order of the elements of a page. It combines a column-following +topological sort of their spatial relations with optional layout semantics (headers, footers, captions), and is +used by all the [exports](../user_guide/exporting.md#reading-order). -.. currentmodule:: doctr.models.reading_order +```{eval-rst} +.. module:: doctr.models.reading_order -.. autofunction:: detect_text_direction +.. autoclass:: doctr.models.reading_order.ReadingOrderPredictor -.. autofunction:: sort_reading_order +.. autofunction:: doctr.models.reading_order.sort_reading_order -.. autofunction:: resolve_reading_segments +.. autofunction:: doctr.models.reading_order.detect_text_direction -.. autofunction:: assign_layout_labels +.. autofunction:: doctr.models.reading_order.resolve_reading_segments -.. autofunction:: deskew_reading_geometries +.. autofunction:: doctr.models.reading_order.assign_layout_labels -.. autoclass:: ReadingOrderPredictor +.. autofunction:: doctr.models.reading_order.deskew_reading_geometries +``` diff --git a/docs/source/api/transforms.md b/docs/source/api/transforms.md new file mode 100644 index 0000000000..67d5aa1db8 --- /dev/null +++ b/docs/source/api/transforms.md @@ -0,0 +1,70 @@ +# doctr.transforms + +```{eval-rst} +.. module:: doctr.transforms +``` + +Data transformations for training and inference. Inspired by +[torchvision](https://github.com/pytorch/vision), they are expressed as composable modules. Image transforms apply to +the image only; sample transforms (wrapped in {class}`SampleCompose`) also update the target, e.g. the boxes of a +rotated image. + +```python +from torchvision.transforms.v2 import Compose + +from doctr import transforms as T + +img_transforms = Compose([ + T.Resize((32, 128), preserve_aspect_ratio=True), + T.RandomApply(T.ColorInversion(), 0.1), + T.RandomApply(T.GaussianNoise(mean=0.1, std=0.1), 0.1), +]) +``` + +## Image transformations + +```{eval-rst} +.. autoclass:: Resize + +.. autoclass:: RandomResize + +.. autoclass:: GaussianNoise + +.. autoclass:: GaussianBlur + +.. autoclass:: ChannelShuffle + +.. autoclass:: ColorInversion + +.. autoclass:: RandomShadow + +.. autoclass:: RandomGlare + +.. autoclass:: RandomLighting +``` + +## Geometric transformations + +```{eval-rst} +.. autoclass:: RandomHorizontalFlip + +.. autoclass:: RandomRotate + +.. autoclass:: RandomCrop + +.. autoclass:: RandomPerspective +``` + +## Composition + +```{eval-rst} +.. autoclass:: SampleCompose + +.. autoclass:: ImageTransform + +.. autoclass:: ImageTorchvisionTransform + +.. autoclass:: OneOf + +.. autoclass:: RandomApply +``` diff --git a/docs/source/api/utils.md b/docs/source/api/utils.md new file mode 100644 index 0000000000..6f43421576 --- /dev/null +++ b/docs/source/api/utils.md @@ -0,0 +1,53 @@ +# doctr.utils + +Complementary tools: visualization, page reconstruction and evaluation metrics. + +## Visualization + +```{eval-rst} +.. currentmodule:: doctr.utils.visualization + +.. autofunction:: visualize_page + +.. autofunction:: visualize_kie_page + +.. autofunction:: draw_boxes +``` + +## Reconstitution + +```{eval-rst} +.. currentmodule:: doctr.utils.reconstitution + +.. autofunction:: synthesize_page + +.. autofunction:: synthesize_kie_page +``` + +(task-evaluation)= +## Task evaluation + +Metrics to evaluate models on each task, used by the evaluation scripts of the references and in the +[model zoo](../models/index.md). + +```{eval-rst} +.. currentmodule:: doctr.utils.metrics + +.. autoclass:: TextMatch + :members: update, summary + +.. autoclass:: LocalizationConfusion + :members: update, summary + +.. autoclass:: OCRMetric + :members: update, summary + +.. autoclass:: DetectionMetric + :members: update, summary + +.. autoclass:: ObjectDetectionMetric + :members: update, summary + +.. autoclass:: TableCellMetric + :members: update, summary +``` diff --git a/docs/source/changelog.md b/docs/source/changelog.md new file mode 100644 index 0000000000..33177ee3ca --- /dev/null +++ b/docs/source/changelog.md @@ -0,0 +1,27 @@ +# Changelog + +Every release of docTR, with a link to its detailed release notes on GitHub. All releases are also listed on the [GitHub releases page](https://github.com/mindee/doctr/releases). + +| Version | Release date | Release notes | +| --- | --- | --- | +| **v1.1.0** | 2026-08-21 | [v1.1.0 on GitHub](https://github.com/mindee/doctr/releases/tag/v1.1.0) | +| **v1.0.1** | 2026-02-04 | [v1.0.1 on GitHub](https://github.com/mindee/doctr/releases/tag/v1.0.1) | +| **v1.0.0** | 2025-07-09 | [v1.0.0 on GitHub](https://github.com/mindee/doctr/releases/tag/v1.0.0) | +| **v0.12.0** | 2025-06-20 | [v0.12.0 on GitHub](https://github.com/mindee/doctr/releases/tag/v0.12.0) | +| **v0.11.0** | 2025-01-30 | [v0.11.0 on GitHub](https://github.com/mindee/doctr/releases/tag/v0.11.0) | +| **v0.10.0** | 2024-10-21 | [v0.10.0 on GitHub](https://github.com/mindee/doctr/releases/tag/v0.10.0) | +| **v0.9.0** | 2024-08-08 | [v0.9.0 on GitHub](https://github.com/mindee/doctr/releases/tag/v0.9.0) | +| **v0.8.1** | 2024-03-04 | [v0.8.1 on GitHub](https://github.com/mindee/doctr/releases/tag/v0.8.1) | +| **v0.8.0** | 2024-02-28 | [v0.8.0 on GitHub](https://github.com/mindee/doctr/releases/tag/v0.8.0) | +| **v0.7.0** | 2023-09-09 | [v0.7.0 on GitHub](https://github.com/mindee/doctr/releases/tag/v0.7.0) | +| **v0.6.0** | 2022-09-29 | [v0.6.0 on GitHub](https://github.com/mindee/doctr/releases/tag/v0.6.0) | +| **v0.5.1** | 2022-03-22 | [v0.5.1 on GitHub](https://github.com/mindee/doctr/releases/tag/v0.5.1) | +| **v0.5.0** | 2021-12-31 | [v0.5.0 on GitHub](https://github.com/mindee/doctr/releases/tag/v0.5.0) | +| **v0.4.1** | 2021-11-22 | [v0.4.1 on GitHub](https://github.com/mindee/doctr/releases/tag/v0.4.1) | +| **v0.4.0** | 2021-10-01 | [v0.4.0 on GitHub](https://github.com/mindee/doctr/releases/tag/v0.4.0) | +| **v0.3.1** | 2021-08-27 | [v0.3.1 on GitHub](https://github.com/mindee/doctr/releases/tag/v0.3.1) | +| **v0.3.0** | 2021-07-02 | [v0.3.0 on GitHub](https://github.com/mindee/doctr/releases/tag/v0.3.0) | +| **v0.2.1** | 2021-05-28 | [v0.2.1 on GitHub](https://github.com/mindee/doctr/releases/tag/v0.2.1) | +| **v0.2.0** | 2021-05-11 | [v0.2.0 on GitHub](https://github.com/mindee/doctr/releases/tag/v0.2.0) | +| **v0.1.1** | 2021-03-18 | [v0.1.1 on GitHub](https://github.com/mindee/doctr/releases/tag/v0.1.1) | +| **v0.1.0** | 2021-03-05 | [v0.1.0 on GitHub](https://github.com/mindee/doctr/releases/tag/v0.1.0) | diff --git a/docs/source/changelog.rst b/docs/source/changelog.rst deleted file mode 100644 index f862b71195..0000000000 --- a/docs/source/changelog.rst +++ /dev/null @@ -1,86 +0,0 @@ -Changelog -========= - -v1.1.0 (2026-08-21) -------------------- -Release note: `v1.1.0 `_ - -v1.0.1 (2026-02-04) -------------------- -Release note: `v1.0.1 `_ - -v1.0.0 (2025-07-09) -------------------- -Release note: `v1.0.0 `_ - -v0.12.0 (2025-06-20) --------------------- -Release note: `v0.12.0 `_ - -v0.11.0 (2025-01-30) --------------------- -Release note: `v0.11.0 `_ - -v0.10.0 (2024-10-21) --------------------- -Release note: `v0.10.0 `_ - -v0.9.0 (2024-08-08) -------------------- -Release note: `v0.9.0 `_ - -v0.8.1 (2024-03-04) -------------------- -Release note: `v0.8.1 `_ - -v0.8.0 (2024-02-28) -------------------- -Release note: `v0.8.0 `_ - -v0.7.0 (2023-09-09) -------------------- -Release note: `v0.7.0 `_ - -v0.6.0 (2022-09-29) -------------------- -Release note: `v0.6.0 `_ - -v0.5.1 (2022-03-22) -------------------- -Release note: `v0.5.1 `_ - -v0.5.0 (2021-12-31) -------------------- -Release note: `v0.5.0 `_ - -v0.4.1 (2021-11-22) -------------------- -Release note: `v0.4.1 `_ - -v0.4.0 (2021-10-01) -------------------- -Release note: `v0.4.0 `_ - -v0.3.1 (2021-08-27) -------------------- -Release note: `v0.3.1 `_ - -v0.3.0 (2021-07-02) -------------------- -Release note: `v0.3.0 `_ - -v0.2.1 (2021-05-28) -------------------- -Release note: `v0.2.1 `_ - -v0.2.0 (2021-05-11) -------------------- -Release note: `v0.2.0 `_ - -v0.1.1 (2021-03-18) -------------------- -Release note: `v0.1.1 `_ - -v0.1.0 (2021-03-05) -------------------- -Release note: `v0.1.0 `_ diff --git a/docs/source/community/index.md b/docs/source/community/index.md new file mode 100644 index 0000000000..b9ca213419 --- /dev/null +++ b/docs/source/community/index.md @@ -0,0 +1,114 @@ +# Community + +docTR is an open-source project developed in the open on [GitHub](https://github.com/mindee/doctr). Whether you need +help, want to share what you built, or would like to contribute, you are welcome here. + +## Get help + +::::{grid} 1 2 3 3 +:gutter: 3 + +:::{grid-item-card} {octicon}`comment-discussion;1.1em` Discussions +:link: https://github.com/mindee/doctr/discussions + +Questions, ideas and show-and-tell. The best place to ask "how do I...?". +::: + +:::{grid-item-card} {octicon}`issue-opened;1.1em` Issues +:link: https://github.com/mindee/doctr/issues + +Bug reports and feature requests. Please include the output of `scripts/collect_env.py`. +::: + +:::{grid-item-card} {octicon}`hubot;1.1em` Ask the docTR Guru +:link: https://gurubase.io/g/doctr + +An AI assistant trained on the docTR documentation and code. +::: +:::: + +Before asking, have a look at [](../user_guide/faq.md). + +## Learn and explore + +::::{grid} 1 2 3 3 +:gutter: 3 + +:::{grid-item-card} {octicon}`book;1.1em` Notebooks +:link: notebooks +:link-type: doc + +Tutorials you can run in Google Colab. +::: + +:::{grid-item-card} {octicon}`tools;1.1em` Community tools +:link: tools +:link-type: doc + +Labeling tool, ONNX runtime, Docling plugin and more. +::: + +:::{grid-item-card} {octicon}`megaphone;1.1em` Articles & videos +:link: resources +:link-type: doc + +What the community wrote and recorded about docTR. +::: +:::: + +## Contribute + +Fixing a typo, adding a vocabulary, implementing a paper: every contribution counts. The +{doc}`contribution guide <../contributing/contributing>` explains how to set up a development environment, run the +tests and build this documentation. Please also read the {doc}`code of conduct <../contributing/code_of_conduct>`. + +## Professional support + +docTR was originally created by [Mindee](https://mindee.com) and is now developed and maintained by +[t2k GmbH](https://www.text2knowledge.de). t2k offers professional services around docTR: + +- **Custom OCR development**: detection, recognition and pipeline work tailored to your documents +- **Project implementation**: turning your ideas into production-ready docTR integrations +- **Technical consulting**: architecture, deployment and integration guidance +- **Ongoing development**: feature extensions and implementation support +- **Workshops & advisory**: hands-on sessions and recommendations for working with docTR + +Contact [info@text2knowledge.de](mailto:info@text2knowledge.de) or visit [text2knowledge.de](https://www.text2knowledge.de) +to discuss your project. + +## Cite docTR + +If docTR helps your research, please cite it: + +```bibtex +@misc{doctr2021, + title={docTR: Document Text Recognition}, + author={Mindee}, + year={2021}, + publisher={GitHub}, + howpublished={\url{https://github.com/mindee/doctr}} +} +``` + +## License + +docTR is distributed under the [Apache 2.0 License](https://github.com/mindee/doctr/blob/main/LICENSE): you can use +it in commercial products. + +```{toctree} +:hidden: +:caption: Learn + +notebooks +tools +resources +``` + +```{toctree} +:hidden: +:caption: Contribute + +../contributing/contributing +../contributing/code_of_conduct +../changelog +``` diff --git a/docs/source/community/notebooks.rst b/docs/source/community/notebooks.rst new file mode 120000 index 0000000000..ff8927cf00 --- /dev/null +++ b/docs/source/community/notebooks.rst @@ -0,0 +1 @@ +../../../notebooks/README.rst \ No newline at end of file diff --git a/docs/source/community/resources.md b/docs/source/community/resources.md new file mode 100644 index 0000000000..5baedafbd1 --- /dev/null +++ b/docs/source/community/resources.md @@ -0,0 +1,43 @@ +# Articles & videos + +Resources created by the docTR community. Wrote or recorded something about docTR? Open a pull request to add it. + +::::{grid} 1 1 2 2 +:gutter: 3 + +:::{grid-item-card} {octicon}`book;1.1em` Fine-tuning OCR works really well +:link: https://www.christianmoscardi.com/blog/2024/10/03/digitizing-us-statistical-abstracts.html + +Digitizing the Statistical Abstracts of the United States, by [Christian Moscardi](https://github.com/cmoscardi). +::: + +:::{grid-item-card} {octicon}`video;1.1em` Mindee docTR — probably the best open-source OCR +:link: https://youtu.be/3nYPIDCToes?si=P3xKIHIiv335x0Ct + +A video by [Andrej Baranovskij](https://github.com/abaranovskis-redsamurai). +::: + +:::{grid-item-card} {octicon}`book;1.1em` Extract text from images in Python with docTR +:link: https://medium.com/@netrajpatil12mati/extract-text-from-images-in-python-with-doctr-455970878507 + +An article by Netraj Patil. +::: + +:::{grid-item-card} {octicon}`book;1.1em` How to detect text in images with OCR +:link: https://blog.roboflow.com/ocr-api/ + +The Roboflow integration, by James Gallagher. +::: + +:::{grid-item-card} {octicon}`book;1.1em` Our search for the best OCR tool in 2023 +:link: https://source.opennews.org/articles/our-search-best-ocr-tool-2023/ + +What we found, by Sanjin Ibrahimovic. +::: + +:::{grid-item-card} {octicon}`book;1.1em` Real-time information extraction from documents with docTR +:link: https://analyticsindiamag.com/ai-mysteries/real-time-information-extraction-from-documents-with-doctr/ + +An article by Yugesh Verma. +::: +:::: diff --git a/docs/source/community/resources.rst b/docs/source/community/resources.rst deleted file mode 100644 index 3332e765c4..0000000000 --- a/docs/source/community/resources.rst +++ /dev/null @@ -1,23 +0,0 @@ -Community Resources -=================== - -This section contains some cool resources created by the docTR community. - - -* |:book:| Fine-tuning OCR works really well: the Statistical Abstracts of the United States: - `Article `_ created by: `Christian Moscardi `_. - -* |:video_camera:| Mindee docTR - Probably the Best Open-Source OCR: - `Video `_ created by: `Andrej Baranovskij `_. - -* |:book:| Extract Text from images in Python with docTR: - `Article `_ created by: `Netraj Patil`. - -* |:book:| How to Detect Text in Images with OCR (Roboflow integration): - `Article `_ created by: `James Gallagher`. - -* |:book:| Our search for the best OCR tool in 2023, and what we found: - `Article `_ created by: `Sanjin Ibrahimovic`. - -* |:book:| Real-time information extraction from documents with docTR: - `Article `_ created by: `Yugesh Verma`. diff --git a/docs/source/community/tools.md b/docs/source/community/tools.md new file mode 100644 index 0000000000..689ea71b0b --- /dev/null +++ b/docs/source/community/tools.md @@ -0,0 +1,41 @@ +# Community tools + +Projects built by the community on top of docTR. Built something useful? Open a pull request to add it to this page, +with a short description and what makes it unique. + +## docTR Labeler + +[github.com/text2knowledge/docTR-Labeler](https://github.com/text2knowledge/docTR-Labeler) + +An annotation tool to create and correct OCR datasets for training and fine-tuning docTR models. + +- **Polygon editing**: draw and edit polygons around text regions, with automatic shape correction +- **Auto-annotation**: suggestions and polygon refinement powered by OnnxTR +- **Fast workflow**: keyboard shortcuts for selection, zoom, undo/redo and saving +- **Flexible**: launch it with the `doctr-labeler` command or use its Python API +- **Private**: everything runs locally, no account needed + +## OnnxTR + +[github.com/felixdittrich92/OnnxTR](https://github.com/felixdittrich92/OnnxTR) + +An [ONNX Runtime](https://onnxruntime.ai) backend for docTR models: the same interface, without PyTorch. See +[](../deployment/onnx.md). + +- **Minimal dependencies**: no PyTorch or TensorFlow +- **Fast inference**: CPU, GPU and accelerators such as OpenVINO +- **Quantization**: 8-bit models for a smaller memory footprint +- **Familiar API**: `onnxtr.models.ocr_predictor`, just like docTR +- **Production ready**: Docker images and Hugging Face Hub integration + +## docling-OCR-OnnxTR + +[github.com/felixdittrich92/docling-OCR-OnnxTR](https://github.com/felixdittrich92/docling-OCR-OnnxTR) + +A plugin that brings the OnnxTR OCR engine to the [Docling](https://github.com/docling-project/docling) document +parsing framework. + +- **Native integration**: use it in Docling pipelines through `OnnxtrOcrOptions` +- **Drop-in replacement** for the other OCR engines of Docling +- **Configurable**: model choice, languages, confidence thresholds, batch sizes, orientation correction and 8-bit + models diff --git a/docs/source/community/tools.rst b/docs/source/community/tools.rst deleted file mode 100644 index 14eb26af89..0000000000 --- a/docs/source/community/tools.rst +++ /dev/null @@ -1,90 +0,0 @@ -Community Tools -=============== - -This section highlights notable tools developed by the docTR community. - - -docTR-Labeler -------------- - -:Link: https://github.com/text2knowledge/docTR-Labeler - -**Overview** - -``docTR-Labeler`` is a dedicated annotation tool tailored for creating and editing OCR datasets to train and fine-tune docTR models. It offers a user-friendly graphical interface, featuring polygon-based text labeling, automatic annotation suggestions via OnnxTR, and convenient label export capabilities. - -**Key Features** - -* Interactive Polygon Editing: Draw and edit polygons around text regions with precision -* AI-Powered Auto-Annotation: Automatic annotation suggestions and polygon refinement powered by OnnxTR -* Auto-Correction: Automatic correction of polygon shapes to ensure accurate text region representation -* Efficient Workflow: Keyboard shortcuts for selection, zooming, undo/redraw, and saving operations -* Flexible Access: CLI launch with ``doctr-labeler`` command and full programmatic Python API integration -* Privacy-First: No authentication required - everything runs locally on your machine -* Real-Time Rendering: Live image rendering with helpful visual feedback - - -OnnxTR ------- - -:Link: https://github.com/felixdittrich92/OnnxTR - -**Overview** - -``OnnxTR`` provides an ONNX-based backend for docTR models, enabling fast, cross-platform inference using ONNX Runtime. It's a core refactored library that enhances the performance and flexibility of OCR tasks without relying on heavy frameworks like PyTorch or TensorFlow. - -**Key Features** - -* Minimal Dependencies: No PyTorch or TensorFlow requirements -* Fast Inference: Optimized with ONNX Runtime for production environments -* Quantization Support: Reduced memory usage and faster inference through model quantization -* Batch Processing: Efficient batch inference capabilities -* Multi-Platform: CPU, GPU, and specialized accelerator runtimes like OpenVINO -* Flexible Installation: Separate install options for different runtime requirements -* Familiar API: One-line inference via ``onnxtr.models.ocr_predictor`` (similar to docTR) -* Docker Ready: Production-ready Docker images available -* Hugging Face Integration: Seamless model sharing and loading -* Server Optimized: OpenCV headless installation options for server environments - - -docling-OCR-OnnxTR ------------------- - -:Link: https://github.com/felixdittrich92/docling-OCR-OnnxTR - -**Overview** - -``docling-OCR-OnnxTR`` is a high-performance plugin that integrates the OnnxTR OCR engine into the Docling document parsing framework. By leveraging ONNX Runtime, it delivers superior accuracy and efficiency compared to traditional OCR engines across various hardware configurations. - -**Key Features** - -* Native Docling Support: Direct integration with Docling pipelines using ``OnnxtrOcrOptions`` -* Drop-in Replacement: Easy migration from existing OCR engines -* Model Selection: Control over detection and recognition model choices -* Multi-Language Support: Configurable language settings -* Quality Control: Adjustable confidence thresholds -* Performance Tuning: Batch size optimization -* Enhanced Processing: Orientation correction and 8-bit model loading options - - -Contribute Your Tool --------------------- - -**Share Your Innovation** - -Have you built something amazing on top of docTR ? - -We'd love to showcase your work! Whether it's a useful plugin, dataset preparation tool, or any other docTR-based project, the community would benefit from learning about it. - -**How to Contribute** - -To contribute your tool to the docTR community, please follow these steps: - -1. **GitHub**: Open a pull request with your tool information -2. **Format**: Follow the structure above with clear descriptions and key features - - -.. tip:: - Include a clear tool description and highlight what makes your tool unique or particularly useful to the docTR community. - - This helps others quickly understand its value and how to use it effectively. diff --git a/docs/source/conf.py b/docs/source/conf.py index c57746536d..3c3064cbbb 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -3,18 +3,11 @@ # This program is licensed under the Apache License 2.0. # See LICENSE or go to for full license details. -# Configuration file for the Sphinx documentation builder. -# -# This file only contains a selection of the most common options. For a full -# list see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# +"""Sphinx configuration of the docTR documentation. + +Build it locally from the repository root with ``make docs-single-version`` (output in ``docs/_build``). +""" + import os import sys from datetime import datetime @@ -34,112 +27,329 @@ version = doctr.__version__ release = doctr.__version__ + "-git" +# Version selected in the version switcher. `build.sh` sets it for every deployed version +# ("latest" for the main branch, "v1.1.0", ... for the releases). +DOCS_VERSION = os.environ.get("DOCTR_DOCS_VERSION", "latest") + +# `build.sh` also renders the previous releases with this configuration: their sources still rely on +# a few extensions that the current pages do not use anymore. +LEGACY_BUILD = os.environ.get("DOCTR_DOCS_LEGACY", "0") == "1" + # -- General configuration --------------------------------------------------- -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. extensions = [ "sphinx.ext.autodoc", "sphinx.ext.napoleon", "sphinx.ext.intersphinx", "sphinx.ext.viewcode", - "sphinx.ext.coverage", "sphinx.ext.mathjax", - "sphinx.ext.autosectionlabel", - "sphinxemoji.sphinxemoji", # cf. https://sphinxemojicodes.readthedocs.io/en/stable/ + "myst_parser", + "sphinx_design", "sphinx_copybutton", - "recommonmark", - "sphinx_markdown_tables", - "sphinx_tabs.tabs", ] +if LEGACY_BUILD: + extensions += ["sphinx.ext.autosectionlabel", "sphinxemoji.sphinxemoji", "sphinx_tabs.tabs"] +else: + extensions += ["sphinx_reredirects"] + +source_suffix = {".rst": "restructuredtext", ".md": "markdown"} + +templates_path = ["_templates"] +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "notebooks/*.rst", "**/_generated", "_includes"] + intersphinx_mapping = { "python": ("https://docs.python.org/3", None), + "numpy": ("https://numpy.org/doc/stable/", None), + "torch": ("https://docs.pytorch.org/docs/stable/", None), "pypdfium2": ("https://pypdfium2.readthedocs.io/en/stable/", None), } +# -- MyST (Markdown) --------------------------------------------------------- + +myst_enable_extensions = [ + "attrs_block", + "attrs_inline", + "colon_fence", + "deflist", + "fieldlist", + "html_image", + "substitution", + "tasklist", +] +myst_heading_anchors = 3 +myst_substitutions = { + "version": version, +} + +# -- API documentation ------------------------------------------------------- + +autodoc_member_order = "bysource" +autodoc_typehints = "description" +autodoc_typehints_description_target = "documented_params" +autodoc_preserve_defaults = True +add_module_names = False +python_maximum_signature_line_length = 88 +napoleon_google_docstring = True +napoleon_numpy_docstring = False napoleon_use_ivar = True +napoleon_use_rtype = False -# Add any paths that contain templates here, relative to this directory. -templates_path = ["_templates"] +# -- Copy button ------------------------------------------------------------- -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "notebooks/*.rst"] +# Strip the prompts of doctest / shell snippets when copying them +copybutton_prompt_text = r">>> |\.\.\. |\$ " +copybutton_prompt_is_regexp = True +copybutton_exclude = ".linenos, .gp, .go" +# -- Redirects (pages moved by the documentation refactor) ------------------- -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = "friendly" -pygments_dark_style = "monokai" -highlight_language = "python3" +redirects = { + "getting_started/installing": "installation.html", + "notebooks": "community/notebooks.html", + "using_doctr/using_models": "../user_guide/ocr_predictor.html", + "using_doctr/using_datasets": "../training/datasets.html", + "using_doctr/using_contrib_modules": "../user_guide/artefacts.html", + "using_doctr/sharing_models": "../training/sharing_models.html", + "using_doctr/using_model_export": "../deployment/performance.html", + "using_doctr/custom_models_training": "../training/train.html", + "using_doctr/running_on_aws": "../deployment/configuration.html", + "using_doctr/using_cli": "../user_guide/cli.html", + "modules/contrib": "../api/contrib.html", + "modules/datasets": "../api/datasets.html", + "modules/io": "../api/io.html", + "modules/models": "../api/models.html", + "modules/transforms": "../api/transforms.html", + "modules/utils": "../api/utils.html", +} # -- Options for HTML output ------------------------------------------------- -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -html_theme = "furo" +html_theme = "pydata_sphinx_theme" +html_title = "docTR documentation" +html_short_title = "docTR" +html_favicon = "_static/images/favicon.ico" +html_static_path = ["_static"] +html_css_files = ["css/doctr.css"] +html_js_files = ["js/doctr.js"] +html_show_sourcelink = False +html_copy_source = False + +html_context = { + "github_user": "mindee", + "github_repo": "doctr", + "github_version": "main", + "doc_path": "docs/source", + "default_mode": "auto", +} -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -# html_theme_options = { - "sidebar_hide_name": True, - "navigation_with_keys": True, - "light_css_variables": { - "color-sidebar-background": "#082747", - "color-sidebar-background-border": "#082747", - "color-sidebar-caption-text": "white", - "color-sidebar-link-text--top-level": "white", - "color-sidebar-link-text": "white", - "sidebar-caption-font-size": "normal", - "color-sidebar-item-background--hover": " #5dade2", + "logo": { + "image_light": "_static/images/logo-light.png", + "image_dark": "_static/images/logo-dark.png", + "alt_text": "docTR - Home", + }, + "navbar_align": "left", + "navbar_start": ["navbar-logo", "version-switcher"], + "navbar_end": ["theme-switcher", "navbar-icon-links"], + "header_links_before_dropdown": 4 if LEGACY_BUILD else 7, + "switcher": { + "json_url": "_static/switcher.json", + "version_match": DOCS_VERSION, }, - "dark_css_variables": { - "color-sidebar-background": "#1a1c1e", - "color-sidebar-background-border": "#1a1c1e", - "color-sidebar-caption-text": "white", - "color-sidebar-link-text--top-level": "white", + "show_version_warning_banner": not LEGACY_BUILD, + "icon_links": [ + { + "name": "GitHub", + "url": "https://github.com/mindee/doctr", + "icon": "fa-brands fa-github", + }, + { + "name": "PyPI", + "url": "https://pypi.org/project/python-doctr/", + "icon": "fa-brands fa-python", + }, + { + "name": "Live demo on Hugging Face", + "url": "https://huggingface.co/spaces/mindee/doctr", + "icon": "fa-solid fa-wand-magic-sparkles", + }, + ], + "use_edit_page_button": not LEGACY_BUILD, + "secondary_sidebar_items": { + "**": ["page-toc", "edit-this-page"], + "index": [], }, + "show_toc_level": 2, + "show_nav_level": 1, + "navigation_depth": 3, + "show_prev_next": True, + "back_to_top_button": True, + "search_as_you_type": True, + "footer_start": ["copyright"], + "footer_center": ["maintainers"], + "footer_end": ["license"], + "pygments_light_style": "github-light", + "pygments_dark_style": "github-dark", + "analytics": {"google_analytics_id": "G-40DVRMX8T4"}, } -html_logo = "_static/images/Logo-docTR-white.png" -html_favicon = "_static/images/favicon.ico" -html_title = "docTR documentation" - - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ["_static"] +# The landing page is a full-width page +html_sidebars = {"index": []} # A list of files that should not be packed into the epub file. epub_exclude_files = ["search.html"] -def add_ga_javascript(app, pagename, templatename, context, doctree): - # Add googleanalytics id - # ref: https://github.com/orenhecht/googleanalytics/blob/master/sphinxcontrib/googleanalytics.py +# -- Generated content --------------------------------------------------------- + +# Vocabularies listed on `models/vocabs.md`, grouped by script. Any vocabulary missing from these groups is listed +# under "Other scripts", so that the page always reflects `doctr.datasets.VOCABS`. +VOCAB_GROUPS = { + "General purpose": ["latin", "english", "multilingual", "latex"], + "Latin script": [ + "albanian", + "afrikaans", + "azerbaijani", + "basque", + "bosnian", + "catalan", + "croatian", + "czech", + "danish", + "dutch", + "estonian", + "esperanto", + "french", + "finnish", + "frisian", + "galician", + "german", + "hausa", + "hungarian", + "icelandic", + "indonesian", + "irish", + "italian", + "latvian", + "lithuanian", + "luxembourgish", + "malagasy", + "malay", + "maltese", + "maori", + "montenegrin", + "norwegian", + "polish", + "portuguese", + "quechua", + "romanian", + "scottish_gaelic", + "serbian_latin", + "slovak", + "slovene", + "somali", + "spanish", + "swahili", + "swedish", + "tagalog", + "turkish", + "uzbek_latin", + "vietnamese", + "welsh", + "yoruba", + "zulu", + ], # fmt: skip + "Cyrillic script": [ + "russian", + "belarusian", + "tatar", + "tajik", + "kazakh", + "kyrgyz", + "bulgarian", + "macedonian", + "mongolian", + "yakut", + "serbian_cyrillic", + "uzbek_cyrillic", + "ukrainian", + ], # fmt: skip + "Greek script": ["greek", "greek_extended"], + "Hebrew & Arabic scripts": ["hebrew", "arabic", "persian", "urdu", "pashto", "kurdish", "uyghur", "sindhi"], + "Indic scripts": [ + "devanagari", + "hindi", + "sanskrit", + "marathi", + "nepali", + "gujarati", + "bengali", + "tamil", + "telugu", + "kannada", + "sinhala", + "malayalam", + "punjabi", + "odia", + ], # fmt: skip + "Southeast Asian & other scripts": [ + "thai", + "lao", + "khmer", + "burmese", + "javanese", + "sudanese", + "armenian", + "georgian", + "ethiopic", + ], # fmt: skip + "East Asian scripts": ["japanese", "korean", "simplified_chinese"], +} + + +def _generate_vocab_tables(app): + """Write the tables of the supported vocabularies, included by `models/vocabs.md`""" + from html import escape + + from doctr.datasets import VOCABS + + english = set(VOCABS["english"]) + listed = {name for names in VOCAB_GROUPS.values() for name in names} + first_lang = list(VOCABS).index("latin") # the entries before are building blocks (digits, punctuation, ...) + others = [name for name in list(VOCABS)[first_lang:] if name not in listed] + groups = {**VOCAB_GROUPS, **({"Other scripts": others} if others else {})} + + parts = [] + for title, names in groups.items(): + rows = [] + for name in names: + if name not in VOCABS: + continue + chars = VOCABS[name] + extra = "".join(c for c in chars if c not in english) + if title == "Latin script" and len(extra) < len(chars) / 2: + summary = f'english + {escape(extra)}' if extra else "english" + else: + summary = escape(chars[:60]) + ("…" if len(chars) > 60 else "") + rows.append( + f"{name}{len(chars)}" + f'
        {summary}
        {escape(chars)}
        ' + "
        " + ) + parts.append( + f"### {title}\n\n```{{raw}} html\n" + '
        ' + "" + f"{''.join(rows)}
        NameSizeCharacters
        \n```\n" + ) - metatags = context.get("metatags", "") - metatags += """ - - - - """.format(app.config.googleanalytics_id) - context["metatags"] = metatags + out_dir = os.path.join(app.srcdir, "models", "_generated") + os.makedirs(out_dir, exist_ok=True) + with open(os.path.join(out_dir, "vocabs.md"), "w", encoding="utf-8") as f: + f.write("\n".join(parts)) def setup(app): - app.add_config_value("googleanalytics_id", "G-40DVRMX8T4", "html") - app.add_css_file("css/mindee.css") - app.add_js_file("js/custom.js") - app.connect("html-page-context", add_ga_javascript) + if not LEGACY_BUILD: + app.connect("builder-inited", _generate_vocab_tables) diff --git a/docs/source/deployment/configuration.md b/docs/source/deployment/configuration.md new file mode 100644 index 0000000000..7e93aa2a6c --- /dev/null +++ b/docs/source/deployment/configuration.md @@ -0,0 +1,64 @@ +# Configuration + +docTR has no configuration file: predictors are configured in code, and a few environment variables control how +the library interacts with the system. + +## Environment variables + +| Variable | Default | Effect | +| --- | --- | --- | +| `DOCTR_CACHE_DIR` | `~/.cache/doctr` | Where downloaded model weights (`models/`) and datasets (`datasets/`) are stored | +| `DOCTR_MULTIPROCESSING_DISABLE` | unset | Set to `TRUE` to disable the thread pools used for data processing, e.g. where `/dev/shm` is not writable | + +## Model cache + +Pretrained weights are downloaded the first time a model is used, then loaded from the cache. Their integrity is +checked with the hash in the file name. + +```bash +export DOCTR_CACHE_DIR=/opt/models/doctr +``` + +### Offline use + +To run on a machine without internet access, populate the cache on a connected machine, then copy it: + +```python +# On a connected machine: instantiating the predictors downloads their weights +from doctr.models import ocr_predictor + +ocr_predictor(pretrained=True) # detection + recognition +ocr_predictor(pretrained=True, assume_straight_pages=False) # + orientation classifiers +ocr_predictor(pretrained=True, detect_tables=True) # + layout and table models +``` + +Copy the cache directory (`~/.cache/doctr` or `$DOCTR_CACHE_DIR`) to the same location on the offline machine. +Alternatively, load the weight files from any path with `model.from_pretrained("path/to/weights.pt")` (see +[](../training/custom_models.md)). + +## AWS Lambda + +The [AWS Lambda](https://aws.amazon.com/lambda/) runtime only allows writing to `/tmp`, and does not provide the +shared memory used by multiprocessing. Configure the function with: + +```bash +DOCTR_MULTIPROCESSING_DISABLE=TRUE +DOCTR_CACHE_DIR=/tmp/doctr +``` + +:::{tip} +Downloading the weights at every cold start is slow. Package them in your container image or a Lambda layer, and +point `DOCTR_CACHE_DIR` to their location if it is writable, or load them with `from_pretrained`. +::: + +The same settings apply to other restricted environments (read-only containers, some serverless platforms). + +## Logging + +docTR logs through the standard {mod}`logging` module, under the `doctr` logger: + +```python +import logging + +logging.getLogger("doctr").setLevel(logging.WARNING) +``` diff --git a/docs/source/deployment/docker.md b/docs/source/deployment/docker.md new file mode 100644 index 0000000000..2abcaa688a --- /dev/null +++ b/docs/source/deployment/docker.md @@ -0,0 +1,59 @@ +# Docker + +Prebuilt images with docTR installed are published on the +[GitHub Container Registry](https://github.com/mindee/doctr/pkgs/container/doctr). They are based on CUDA 12.2 and +ready for GPU inference. + +## Run an image + +```bash +docker run -it --gpus all ghcr.io/mindee/doctr: bash +``` + +Drop `--gpus all` to run on the CPU. For GPU support, your host needs: + +- an NVIDIA driver supporting **CUDA 12.2 or newer**, otherwise PyTorch cannot initialize the GPU; +- the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) + so that Docker can access the GPU. + +## Tags + +Tags follow the pattern `-py-`: + +`` +: `torch` for the core package, or `torch-viz-html-contrib` with all the optional extras. + +`` +: The Python version of the image, e.g. `3.11.15`, `3.12.9` or `3.13.14`. + +`` +: A release tag (e.g. `v1.1.0`), or a `YYYY-MM` date for the images rebuilt from the `main` branch every three + months. + +For instance, `torch-viz-html-contrib-py3.11.15-v1.1.0` is release 1.1.0 with all the extras on Python 3.11.15. +Browse the [registry](https://github.com/mindee/doctr/pkgs/container/doctr) for the available tags. + +## Build your own image + +The [`Dockerfile`](https://github.com/mindee/doctr/blob/main/Dockerfile) at the root of the repository accepts +build arguments to choose the Python version, the extras and the docTR version: + +```bash +docker build -t doctr \ + --build-arg FRAMEWORK="torch,viz,html,contrib" \ + --build-arg PYTHON_VERSION=3.11.15 \ + --build-arg DOCTR_VERSION=v1.1.0 \ + . +``` + +| Argument | Default | Description | +| ---------------- | ---------- | ----------------------------------------------------------- | +| `FRAMEWORK` | `torch` | Extras to install, comma-separated (e.g. `torch,viz,html`) | +| `PYTHON_VERSION` | `3.11.15` | Python version, compiled from source | +| `DOCTR_VERSION` | `main` | Git branch, tag or commit of docTR to install | + +:::{tip} +Pretrained weights are downloaded at runtime. To avoid it, warm the cache when building the image, e.g. with +`RUN python -c "from doctr.models import ocr_predictor; ocr_predictor(pretrained=True)"`, or mount a volume on +the cache directory (see [](configuration.md)). +::: diff --git a/docs/source/deployment/index.md b/docs/source/deployment/index.md new file mode 100644 index 0000000000..6d182a1201 --- /dev/null +++ b/docs/source/deployment/index.md @@ -0,0 +1,52 @@ +# Deployment + +Taking docTR to production means making it fast, packaging it, and serving it. This section covers each step. + +::::{grid} 1 2 2 2 +:gutter: 3 + +:::{grid-item-card} {octicon}`rocket;1.1em` Performance +:link: performance +:link-type: doc + +GPU and Apple Silicon, half precision, `torch.compile` and other speed-ups. +::: + +:::{grid-item-card} {octicon}`cpu;1.1em` ONNX & OnnxTR +:link: onnx +:link-type: doc + +Export models to ONNX and run them without PyTorch. +::: + +:::{grid-item-card} {octicon}`container;1.1em` Docker +:link: docker +:link-type: doc + +Prebuilt GPU-ready images, tags, and building your own. +::: + +:::{grid-item-card} {octicon}`globe;1.1em` Web API & demo app +:link: web_api +:link-type: doc + +A FastAPI template to serve docTR, and a Streamlit demo. +::: + +:::{grid-item-card} {octicon}`gear;1.1em` Configuration +:link: configuration +:link-type: doc + +Environment variables, model cache, offline use and AWS Lambda. +::: +:::: + +```{toctree} +:hidden: + +performance +onnx +docker +web_api +configuration +``` diff --git a/docs/source/deployment/onnx.md b/docs/source/deployment/onnx.md new file mode 100644 index 0000000000..0c675d32af --- /dev/null +++ b/docs/source/deployment/onnx.md @@ -0,0 +1,74 @@ +# ONNX & OnnxTR + +[ONNX](https://onnx.ai) is an open format for machine learning models, supported by many runtimes and hardware +vendors. Exporting docTR models to ONNX lets you run them **without PyTorch**, with a smaller footprint and +often a faster CPU inference. + +## Export a model + +{func}`~doctr.models.utils.export_model_to_onnx` traces a model with a dummy input. Build the model with +`exportable=True`, which makes it return raw outputs instead of post-processed predictions: + +```python +import torch + +from doctr.models import vitstr_small +from doctr.models.utils import export_model_to_onnx + +model = vitstr_small(pretrained=True, exportable=True) # or your own fine-tuned model + +batch_size = 1 +input_shape = (3, 32, 128) # (C, H, W), the input shape of the model +dummy_input = torch.rand((batch_size, *input_shape), dtype=torch.float32) + +model_path = export_model_to_onnx(model, model_name="vitstr_small", dummy_input=dummy_input) +``` + +The batch dimension of the exported model is dynamic. Find the input shape of any model in `model.cfg["input_shape"]`. + +## Run it with OnnxTR + +[OnnxTR](https://github.com/felixdittrich92/OnnxTR) is a lightweight package built on +[ONNX Runtime](https://onnxruntime.ai), with the same interface as docTR. It ships ONNX versions of the pretrained +models and loads your exported ones: + +::::{tab-set} + +:::{tab-item} CPU +```bash +pip install "onnxtr[cpu]" +``` +::: + +:::{tab-item} NVIDIA GPU +```bash +pip install "onnxtr[gpu]" +``` +::: + +:::{tab-item} Intel (OpenVINO) +```bash +pip install "onnxtr[openvino]" +``` +::: +:::: + +```python +from onnxtr.io import DocumentFile +from onnxtr.models import linknet_resnet18, ocr_predictor, parseq + +pages = DocumentFile.from_images("path/to/page.jpg") + +# Pretrained models, exactly like in docTR +predictor = ocr_predictor(det_arch="linknet_resnet18", reco_arch="parseq") + +# Or your exported models +det_model = linknet_resnet18("path/to/detection.onnx") +reco_model = parseq("path/to/recognition.onnx", vocab="ABC") # the vocabulary used for training +predictor = ocr_predictor(det_arch=det_model, reco_arch=reco_model) + +result = predictor(pages) +``` + +See the OnnxTR documentation for [installation options](https://github.com/felixdittrich92/OnnxTR#installation) +and [loading custom models](https://github.com/felixdittrich92/OnnxTR#loading-custom-exported-models). diff --git a/docs/source/deployment/performance.md b/docs/source/deployment/performance.md new file mode 100644 index 0000000000..826a56cac8 --- /dev/null +++ b/docs/source/deployment/performance.md @@ -0,0 +1,136 @@ +# Performance + +docTR runs on the CPU out of the box. This page lists the levers to make it faster, from the most effective to the +most specialized. + +## Run on a GPU + +Predictors are PyTorch modules: move them to a device like any model. Inputs are moved automatically. + +::::{tab-set} + +:::{tab-item} NVIDIA (CUDA) +```python +import torch + +from doctr.models import ocr_predictor + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +predictor = ocr_predictor(pretrained=True).to(device) +# or simply: ocr_predictor(pretrained=True).cuda() +``` +::: + +:::{tab-item} Apple Silicon (MPS) +```python +import torch + +from doctr.models import ocr_predictor + +device = torch.device("mps" if torch.backends.mps.is_available() else "cpu") +predictor = ocr_predictor(pretrained=True).to(device) +``` +::: +:::: + +The same applies to every predictor: `detection_predictor`, `recognition_predictor`, `layout_predictor`, +`table_predictor`, `crop_orientation_predictor` and `page_orientation_predictor`. + +## Half precision + +On a GPU, half-precision inference uses 16-bit floats instead of 32-bit: it is faster and uses less memory, with +the same results in practice. + +```python +from doctr.models import ocr_predictor + +predictor = ocr_predictor(pretrained=True).cuda().bfloat16() # or .half() for float16 +result = predictor(pages) +``` + +| Format | Method | Notes | +| --- | --- | --- | +| **bfloat16** {bdg-success}`recommended` | `.bfloat16()` | Same range as float32, so no overflow. Requires an Ampere GPU or newer (compute capability 8.0+) | +| float16 | `.half()` | More precision but a much narrower range. Use it on older GPUs | + +:::{note} +Half precision is only supported on GPU devices. The [CLI](../user_guide/cli.md) enables bfloat16 automatically on +compatible GPUs. +::: + +## Batch sizes + +The predictor processes pages and word crops in batches: `det_bs` pages at a time for detection (default `2`) and +`reco_bs` crops at a time for recognition (default `128`). On a GPU with free memory, larger batches are faster; +lower them if you run out of memory: + +```python +predictor = ocr_predictor(pretrained=True, det_bs=8, reco_bs=512) +``` + +Pass all the pages of a document (or several documents) in **one call** so that batches are full. + +## Skip what you don't need + +- Keep `assume_straight_pages=True` (default) for upright documents: it avoids the orientation classifiers and + the more expensive rotated-box post-processing. +- With rotated documents, `disable_page_orientation=True` and `disable_crop_orientation=True` skip the orientation + classifiers when you know the text is horizontal (see [](../user_guide/rotated_documents.md)). +- Only enable `detect_layout`, `detect_tables`, `detect_orientation` and `detect_language` when you use them. +- Use lighter architectures, see the [model zoo](../models/index.md). + +## Compile the models + +[`torch.compile`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) converts the models into +optimized graphs. Compile the models first, then build the predictor with them: + +```python +import torch + +from doctr.models import ( + crop_orientation_predictor, + fast_base, + mobilenet_v3_small_crop_orientation, + mobilenet_v3_small_page_orientation, + ocr_predictor, + page_orientation_predictor, + vitstr_small, +) + +detection_model = torch.compile(fast_base(pretrained=True).eval()) +recognition_model = torch.compile(vitstr_small(pretrained=True).eval()) + +predictor = ocr_predictor(detection_model, recognition_model, assume_straight_pages=False) + +# Only needed when the orientation classifiers are used (rotated documents) +predictor.crop_orientation_predictor = crop_orientation_predictor( + torch.compile(mobilenet_v3_small_crop_orientation(pretrained=True).eval()) +) +predictor.page_orientation_predictor = page_orientation_predictor( + torch.compile(mobilenet_v3_small_page_orientation(pretrained=True).eval()) +) + +result = predictor(pages) +``` + +:::{important} +- The first calls are slow while the graphs are compiled: warm the predictor up before measuring. +- The `master` recognition architecture does not support compilation yet. +- Only the default `inductor` backend is officially supported. +::: + +## CPU inference + +- PyTorch uses all the cores by default. When several workers share a machine, limit each of them with + `torch.set_num_threads(n)`. +- For the smallest footprint, export the models to ONNX and run them with [OnnxTR](onnx.md), optionally with + 8-bit quantized models. + +## Measure + +The `latency.py` scripts of the [references](https://github.com/mindee/doctr/tree/main/references) benchmark any +architecture on your hardware: + +```bash +python references/detection/latency.py fast_base --it 100 +``` diff --git a/docs/source/deployment/web_api.md b/docs/source/deployment/web_api.md new file mode 100644 index 0000000000..b1babbeb67 --- /dev/null +++ b/docs/source/deployment/web_api.md @@ -0,0 +1,80 @@ +# Web API & demo app + +The repository contains two ready-to-run applications: a REST API template to integrate docTR into your services, +and an interactive demo to try the models. + +## REST API + +The [`api/`](https://github.com/mindee/doctr/tree/main/api) folder is a [FastAPI](https://fastapi.tiangolo.com) +application exposing docTR over HTTP, packaged with Docker. + +### Start the server + +You need [Docker](https://docs.docker.com/get-docker/) and Python (to resolve the dependencies with +[uv](https://docs.astral.sh/uv/)): + +```bash +git clone https://github.com/mindee/doctr.git +cd doctr/api +make run +``` + +The server listens on port **8080**. Its interactive documentation (Swagger UI) is available at +[http://localhost:8080/docs](http://localhost:8080/docs). + +### Routes + +| Route | Description | +| --- | --- | +| `POST /detection` | Text detection: the boxes of the words | +| `POST /recognition` | Text recognition of word crops | +| `POST /ocr` | End-to-end OCR | +| `POST /kie` | Key information extraction | + +Each route accepts images and PDFs (`application/pdf`, `image/jpeg`, `image/png`) as multipart files, and the +architectures to use as query parameters: + +```python +import requests + +params = {"det_arch": "fast_base", "reco_arch": "crnn_vgg16_bn"} + +with open("path/to/invoice.pdf", "rb") as f: + files = [("files", ("invoice.pdf", f.read(), "application/pdf"))] + +response = requests.post("http://localhost:8080/ocr", params=params, files=files) +print(response.json()) +``` + +The template is a starting point: adapt the routes, the response schemas and the models to your needs. The +[API README](https://github.com/mindee/doctr/tree/main/api#readme) documents every route with examples. + +## Demo app + +A [Streamlit](https://streamlit.io) app lets you upload a document, pick the models, and inspect the +segmentation maps, the detected words and the reconstructed page. + +```{image} ../_static/images/demo.png +:alt: The docTR demo app +:align: center +``` + +Try it online on [Hugging Face Spaces](https://huggingface.co/spaces/mindee/doctr), or run it locally from the +repository: + +```bash +pip install -r demo/pt-requirements.txt +streamlit run demo/app.py +``` + +## Example script + +[`scripts/analyze.py`](https://github.com/mindee/doctr/blob/main/scripts/analyze.py) analyzes a single file and +displays the results, a handy way to try options from the terminal: + +```bash +python scripts/analyze.py path/to/your/doc.pdf +python scripts/analyze.py --help +``` + +For batch processing from the terminal, use the [CLI](../user_guide/cli.md). diff --git a/docs/source/getting_started/concepts.md b/docs/source/getting_started/concepts.md new file mode 100644 index 0000000000..1aa2ce6d56 --- /dev/null +++ b/docs/source/getting_started/concepts.md @@ -0,0 +1,176 @@ +# Core concepts + +A few ideas come back on every page of this documentation: **predictors**, the **`Document`** tree they return, +and the way docTR expresses **positions**. Ten minutes here will make everything else easier to follow. + +## The OCR pipeline + +OCR is split into two consecutive tasks, each solved by a dedicated deep learning model: + +1. **Text detection** localizes every word on the page (*where is text?*). +2. **Text recognition** transcribes each detected word from its crop (*what does it say?*). + +The predictions are then assembled into a structured document. Optional stages plug into the same pipeline when +you need them: orientation handling for rotated pages, layout analysis, and table structure recognition. + +```{raw} html +:file: ../_includes/pipeline.html +``` + +## Predictors and models + +You rarely call a model directly. Instead you use a **predictor**: a callable that takes care of everything +around the neural network so you can feed it raw pages. + +| Layer | Responsibility | Example | +| ----------------- | ---------------------------------------------------------------------------------- | -------------------------------- | +| **Pre-processor** | Resizes, pads, normalizes and batches the input images | {class}`~doctr.models.preprocessor.PreProcessor` | +| **Model** | A regular PyTorch `nn.Module` that runs the network | `fast_base`, `crnn_vgg16_bn` | +| **Post-processor**| Turns raw network outputs into boxes, strings and scores | decoding of the segmentation map | + +docTR exposes a factory function for every task. Each one accepts an architecture **name** (or a model +**instance**, e.g. one you trained yourself) and returns a ready-to-use predictor: + +| Task | Factory | Returns | +| --------------------------- | ----------------------------------------------------------- | ----------------------------------------- | +| End-to-end OCR | {func}`~doctr.models.ocr_predictor` | a {class}`~doctr.io.Document` | +| Key information extraction | {func}`~doctr.models.kie_predictor` | a {class}`~doctr.io.KIEDocument` | +| Text detection only | {func}`~doctr.models.detection.detection_predictor` | boxes per page | +| Text recognition only | {func}`~doctr.models.recognition.recognition_predictor` | `(text, confidence)` per crop | +| Layout analysis | {func}`~doctr.models.layout.layout_predictor` | labelled regions per page | +| Table structure recognition | {func}`~doctr.models.table_structure.table_predictor` | cells with row/column spans per table | + +The end-to-end {func}`~doctr.models.ocr_predictor` composes the task predictors, which stay reachable as attributes: + +```python +from doctr.models import ocr_predictor + +predictor = ocr_predictor(pretrained=True) + +predictor.det_predictor # text detection (pre-processor + model + post-processor) +predictor.reco_predictor # text recognition +predictor.doc_builder # assembles words into lines, blocks and pages +``` + +:::{important} +Always pass `pretrained=True` unless you load your own weights: without it, the models are randomly initialized +and return meaningless predictions. +::: + +## The `Document` tree + +The OCR predictor returns a {class}`~doctr.io.Document`: a tree of elements that mirrors the structure of what was +read. + +```{raw} html +
        Document +└── pages: list[Page] # one per input page + ├── blocks: list[Block] # groups of lines (paragraphs, addresses...) + │ ├── lines: list[Line] # words read together + │ │ └── words: list[Word] # value, confidence, geometry + │ └── artefacts: list[Artefact] # non-textual elements + ├── layout: list[LayoutElement] # with detect_layout=True + └── tables: list[Table] # with detect_tables=True + └── cells: list[TableCell]
        +``` + +| Element | What it represents | +| ---------------------------------------- | ---------------------------------------------------------------------------------------------------- | +| {class}`~doctr.io.Word` | An uninterrupted sequence of characters, with its text `value`, recognition `confidence` and `geometry` | +| {class}`~doctr.io.Line` | Words aligned and meant to be read together. On a two-column page, a horizontal row holds two lines | +| {class}`~doctr.io.Block` | A group of lines (e.g. an address written on several lines) and artefacts | +| {class}`~doctr.io.Artefact` | A non-textual element: a picture, a logo, a QR code, a signature... | +| {class}`~doctr.io.LayoutElement` | A region found by the layout model: `Title`, `Text`, `Table`, `Picture`, `Page-header`... | +| {class}`~doctr.io.Table` / {class}`~doctr.io.TableCell` | A recognized table, as cells spanning rows and columns | +| {class}`~doctr.io.Page` | Everything found on one page, plus its `dimensions`, `orientation` and `language` | + +Every element can be **rendered** to text with `.render()` and **exported** to a JSON-serializable dict with +`.export()`. See [](../user_guide/results.md) for a hands-on tour. + +## Coordinates + +All geometries are expressed in **relative coordinates**, between `0` and `1`, as a fraction of the page width (x) +and height (y), with the origin at the **top-left** corner. This makes them independent of the resolution the page +was rendered at. + +::::{grid} 1 1 2 2 +:gutter: 3 + +:::{grid-item-card} Straight boxes (default) +Two points: the top-left and the bottom-right corners. + +```python +((xmin, ymin), (xmax, ymax)) +# e.g. ((0.12, 0.05), (0.31, 0.08)) +``` +::: + +:::{grid-item-card} Rotated boxes +With `assume_straight_pages=False`: a polygon of four `(x, y)` points, as a NumPy array of shape `(4, 2)`. + +```python +array([[x1, y1], [x2, y2], [x3, y3], [x4, y4]]) +``` +::: +:::: + +To get pixel coordinates, multiply by the page size. Note that `page.dimensions` is `(height, width)`: + +```python +height, width = page.dimensions +(xmin, ymin), (xmax, ymax) = word.geometry +box_in_pixels = (int(xmin * width), int(ymin * height), int(xmax * width), int(ymax * height)) +``` + +## Scores + +docTR attaches several scores to its predictions. They answer different questions: + +`Word.confidence` +: How sure the **recognition** model is about the text, between 0 and 1. By default it is the lowest + probability among the characters of the word (see [confidence aggregation](../user_guide/ocr_predictor.md#confidence-scores)). + +`Word.objectness_score` +: How sure the **detection** model is that the box contains text. + +`Word.crop_orientation` +: The predicted rotation of the word crop, `{"value": angle, "confidence": score}`, when crop orientation is + classified (rotated documents). + +`Page.orientation` / `Page.language` +: The estimated page rotation and language, filled with `detect_orientation=True` / `detect_language=True`. + +## Vocabularies + +A recognition model can only output characters from the **vocabulary** it was trained on. The pretrained recognition +models use the `french` vocabulary, which covers Latin characters with the most common accents, digits and +punctuation. docTR ships [100+ vocabularies](../models/vocabs.md) for training models on other languages and +alphabets, and lets you [restrict a model to a subset](../user_guide/ocr_predictor.md#restrict-the-output-characters) +of its vocabulary. + +```python +from doctr.models import recognition_predictor + +predictor = recognition_predictor("crnn_vgg16_bn", pretrained=True) +print(predictor.model.cfg["vocab"]) +``` + +## Next steps + +::::{grid} 1 2 2 2 +:gutter: 3 + +:::{grid-item-card} {octicon}`book;1.1em` User guide +:link: ../user_guide/index +:link-type: doc + +Every option of the OCR predictor, explained with examples. +::: + +:::{grid-item-card} {octicon}`stack;1.1em` Model zoo +:link: ../models/index +:link-type: doc + +Compare the architectures and pick the right one for your documents. +::: +:::: diff --git a/docs/source/getting_started/index.md b/docs/source/getting_started/index.md new file mode 100644 index 0000000000..0641a37844 --- /dev/null +++ b/docs/source/getting_started/index.md @@ -0,0 +1,43 @@ +# Get started + +New to docTR? These three pages take you from `pip install` to structured OCR results, and explain the +handful of concepts that the rest of the documentation builds on. + +::::{grid} 1 1 3 3 +:gutter: 3 + +:::{grid-item-card} {octicon}`download;1.1em` Installation +:link: installation +:link-type: doc + +Install the package, the optional extras, or use the Docker images. +::: + +:::{grid-item-card} {octicon}`play;1.1em` Quickstart +:link: quickstart +:link-type: doc + +Run OCR on your first document and export the result in five minutes. +::: + +:::{grid-item-card} {octicon}`light-bulb;1.1em` Core concepts +:link: concepts +:link-type: doc + +How the pipeline works, what a `Document` is made of, and how coordinates are expressed. +::: +:::: + +:::{tip} +Prefer to explore in a notebook? The [Quicktour notebook](https://colab.research.google.com/github/mindee/notebooks/blob/main/doctr/quicktour.ipynb) +runs in Google Colab without installing anything, and the [live demo](https://huggingface.co/spaces/mindee/doctr) +lets you try the models on your own files in the browser. +::: + +```{toctree} +:hidden: + +installation +quickstart +concepts +``` diff --git a/docs/source/getting_started/installation.md b/docs/source/getting_started/installation.md new file mode 100644 index 0000000000..8bdbf25721 --- /dev/null +++ b/docs/source/getting_started/installation.md @@ -0,0 +1,134 @@ +# Installation + +docTR is distributed on PyPI as `python-doctr`. It requires **Python 3.11 or newer** and installs +[PyTorch](https://pytorch.org) as its deep learning backend. + +## Install the package + +::::{tab-set} + +:::{tab-item} pip +```bash +pip install python-doctr +``` +::: + +:::{tab-item} uv +```bash +uv add python-doctr +# or, outside of a uv project +uv pip install python-doctr +``` +::: + +:::{tab-item} From source +Install the latest development version, e.g. to try a feature that is not released yet: + +```bash +pip install "python-doctr @ git+https://github.com/mindee/doctr.git" +``` +::: +:::: + +To check that everything works, print the installed version: + +```bash +python -c "import doctr; print(doctr.__version__)" +``` + +:::{note} +The pretrained weights are **not** part of the package: each model is downloaded the first time you use it and +cached in `~/.cache/doctr` (see [](../deployment/configuration.md) to change this location or to run offline). +::: + +## Optional extras + +The core package is kept light. A few features need extra dependencies, which you can pull in with *extras*: + +| Extra | Installs | Needed for | +| --------- | ----------------------------- | ------------------------------------------------------------------------------------------------ | +| `viz` | `matplotlib`, `mplcursors` | Displaying predictions with {meth}`Document.show() ` | +| `html` | `weasyprint` | Reading web pages with {meth}`DocumentFile.from_url() ` | +| `contrib` | `onnxruntime` | The [artefact detector](../user_guide/artefacts.md) of the `contrib` module | + +```bash +# One extra +pip install "python-doctr[viz]" +# Several extras +pip install "python-doctr[viz,html,contrib]" +``` + +:::{admonition} WeasyPrint system libraries +:class: dropdown + +The `html` extra relies on [WeasyPrint](https://doc.courtbouillon.org/weasyprint/stable/first_steps.html#installation), +which needs Pango on the system. On Debian/Ubuntu: `sudo apt install libpango-1.0-0 libpangoft2-1.0-0 libharfbuzz-subset0`. +On macOS: `brew install pango`. +::: + +## GPU support + +docTR runs on whatever device PyTorch supports. The default `torch` wheel from PyPI already supports NVIDIA GPUs on +Linux; for another CUDA version, ROCm or a CPU-only build, install PyTorch first by following the +[official selector](https://pytorch.org/get-started/locally/), then install docTR: + +```bash +# Example: CPU-only build of PyTorch (smaller download) +pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu +pip install python-doctr +``` + +Check which accelerator PyTorch sees: + +```python +import torch + +print("CUDA:", torch.cuda.is_available()) +print("Apple MPS:", torch.backends.mps.is_available()) +``` + +Models run on the CPU until you move them: see [](../deployment/performance.md) to run them on a GPU or in half precision. + +## Docker images + +Ready-to-use images with docTR preinstalled are published on the +[GitHub Container Registry](https://github.com/mindee/doctr/pkgs/container/doctr). They are GPU-ready (CUDA 12.2): + +```bash +docker run -it --gpus all ghcr.io/mindee/doctr: bash +``` + +Pick a `` from the registry; [](../deployment/docker.md) explains the tag naming scheme and how to build your own image. + +## Development installation + +To contribute to docTR, clone the repository and install it in editable mode with all the development tools: + +```bash +git clone https://github.com/mindee/doctr.git +cd doctr +pip install -e ".[dev]" +pre-commit install +``` + +See the {doc}`contribution guide <../contributing/contributing>` for the tests, the code style and how to build this documentation. + +## Next steps + +::::{grid} 1 2 2 2 +:gutter: 3 + +:::{grid-item-card} {octicon}`play;1.1em` Quickstart +:link: quickstart +:link-type: doc + +Extract the text of your first document. +::: + +:::{grid-item-card} {octicon}`light-bulb;1.1em` Core concepts +:link: concepts +:link-type: doc + +Understand predictors, documents and coordinates. +::: +:::: diff --git a/docs/source/getting_started/installing.rst b/docs/source/getting_started/installing.rst deleted file mode 100644 index d8c99d8f50..0000000000 --- a/docs/source/getting_started/installing.rst +++ /dev/null @@ -1,52 +0,0 @@ - -************ -Installation -************ - -This library requires `Python `_ 3.11 or higher. - - -Via Python Package -================== - -Install the last stable release of the package using `pip `_: - -.. code:: bash - - pip install python-doctr - -We strive towards reducing framework-specific dependencies to a minimum, but some necessary features are developed by third-parties for specific frameworks. To avoid missing some dependencies for a specific framework, you can install specific builds as follows: - -.. code:: bash - - pip install python-doctr - # or with preinstalled packages for visualization & html & contrib module support - pip install "python-doctr[viz,html,contrib]" - -Available optional extras: - -* ``viz``: installs `matplotlib` and `mplcursors` for result visualisation (e.g. ``Page.show()``) -* ``html``: installs `weasyprint` for reading HTML documents via :func:`~doctr.io.read_html` -* ``contrib``: installs `onnxruntime` for the :class:`~doctr.contrib.ArtefactDetector` contrib module - - -Via Git -======= - -Install the library in developer mode: - - -.. code:: bash - - git clone https://github.com/mindee/doctr.git - pip install -e doctr/. - - -Via Docker -========== - -Official Docker images are available on the `GitHub Container Registry `_. - -.. code:: bash - - docker run -it ghcr.io/mindee/doctr:latest bash diff --git a/docs/source/getting_started/quickstart.md b/docs/source/getting_started/quickstart.md new file mode 100644 index 0000000000..2392d023fa --- /dev/null +++ b/docs/source/getting_started/quickstart.md @@ -0,0 +1,249 @@ +# Quickstart + +This page takes you from a PDF to structured text in a few lines of code. You need docTR installed +(`pip install python-doctr`, see [](installation.md)) and a document to read. + +:::{tip} +No document at hand? Download the {download}`sample invoice <../_static/images/examples/invoice.pdf>` used on this +page and save it as `invoice.pdf` next to your script: you will get exactly the outputs shown below. +::: + +```{image} ../_static/images/examples/invoice_input.png +:alt: The sample invoice used on this page +:width: 560px +:align: center +``` + +## 1. Load the document + +{class}`~doctr.io.DocumentFile` turns PDFs, images and web pages into a list of pages: + +```python +from doctr.io import DocumentFile + +pages = DocumentFile.from_pdf("invoice.pdf") +# or: DocumentFile.from_images(["page1.jpg", "page2.jpg"]) +``` + +## 2. Run OCR + +{func}`~doctr.models.ocr_predictor` builds a complete pipeline: a text detection model followed by a text +recognition model. With `pretrained=True`, the weights are downloaded on first use and cached. + +```python +from doctr.models import ocr_predictor + +predictor = ocr_predictor(pretrained=True) +result = predictor(pages) +``` + +That's it: `result` is a {class}`~doctr.io.Document` holding every word, its position and its confidence. + +## 3. Get the text + +{meth}`~doctr.io.Document.render` returns the text of the whole document, in reading order: + +```python +print(result.render()) +``` + +```text +ACME Office Supplies + +INVOICE + +42 Harbour Road, Hamburg + +No. 2026-0412 +Date: 26/10/2026 + +Bill to: +Jane Doe +12 Market Street, Berlin +... +``` + +## 4. Explore the structure + +The result mirrors the document: pages contain blocks, blocks contain lines, lines contain words. +Printing it shows the tree: + +```python +print(result) +``` + +```text +Document( + (pages): [Page( + dimensions=(1684, 1191) + (blocks): [Block( + (lines): [ + Line( + (words): [ + Word(value='ACME', confidence=1.0), + Word(value='Office', confidence=1.0), + Word(value='Supplies', confidence=1.0), + ] + ), + Line( + (words): [Word(value='INVOICE', confidence=1.0)] + ), + ... +``` + +Every element is a regular Python object, so you can walk the tree to get the words with their positions: + +```python +for page in result.pages: + for block in page.blocks: + for line in block.lines: + for word in line.words: + (xmin, ymin), (xmax, ymax) = word.geometry + print( + f"{word.value:<10} conf={word.confidence:.2f} box=({xmin:.3f}, {ymin:.3f}, {xmax:.3f}, {ymax:.3f})" + ) +``` + +```text +ACME conf=1.00 box=(0.071, 0.054, 0.160, 0.071) +Office conf=1.00 box=(0.169, 0.054, 0.258, 0.071) +Supplies conf=1.00 box=(0.269, 0.055, 0.399, 0.073) +INVOICE conf=1.00 box=(0.697, 0.057, 0.855, 0.074) +... +``` + +Geometries are **relative** to the page size: `((xmin, ymin), (xmax, ymax))` with values between 0 and 1. +[](concepts.md#coordinates) shows how to convert them to pixels. + +## 5. Visualize the predictions + +With the `viz` extra installed (`pip install "python-doctr[viz]"`), {meth}`~doctr.io.Document.show` overlays the +predictions on the pages in an interactive window: + +```python +result.show() +``` + +```{image} ../_static/images/examples/invoice_words.png +:alt: Detected words overlaid on the invoice +:width: 560px +:align: center +``` + +## 6. Export the results + +Turn the result into the format your application needs: + +::::{tab-set} + +:::{tab-item} JSON +```python +import json + +with open("invoice.json", "w") as f: + json.dump(result.export(), f) +``` + +The export is a nested dictionary with the same structure as the `Document` (pages → blocks → lines → words). +::: + +:::{tab-item} Markdown +```python +markdown = result.export_as_markdown() +``` +::: + +:::{tab-item} HTML +```python +html = result.export_as_html() +``` +::: + +:::{tab-item} hOCR (XML) +```python +# One (bytes, ElementTree) pair per page +xml_pages = result.export_as_xml() +``` +::: +:::: + +[](../user_guide/exporting.md) covers every format and its options. + +## 7. Go further: layout & tables + +Add `detect_tables=True` and docTR also finds the layout of the page and reconstructs its tables. Here is the +Markdown export of the same invoice: + +```python +predictor = ocr_predictor(pretrained=True, detect_tables=True) +result = predictor(pages) +print(result.export_as_markdown()) +``` + +```markdown +## ACME Office Supplies + +42 Harbour Road, Hamburg + +## Bill to: + +Jane Doe + +12 Market Street, Berlin + +INVOICE + +No. 2026-0412 +Date: 26/10/2026 + +| Description | Qty | Unit price | Amount | +| --- | --- | --- | --- | +| Recycled paper A4 | 10 | 8.50 | 85.00 | +| Ballpoint pens (box) | 5 | 12.00 | 60.00 | +| Desk lamp | 2 | 45.00 | 90.00 | +| Office chair | 1 | 1,015.00 | 1,015.00 | + +Total due + +1,250.00 EUR +... +``` + +:::{admonition} Prefer the command line? +:class: tip + +The `doctr-cli` command runs the same pipeline without writing any code: + +```bash +doctr-cli --input_path invoice.pdf --detect_tables --output invoice.md +``` + +See [](../user_guide/cli.md). +::: + +## Next steps + +::::{grid} 1 2 3 3 +:gutter: 3 + +:::{grid-item-card} {octicon}`light-bulb;1.1em` Core concepts +:link: concepts +:link-type: doc + +Predictors, the `Document` tree, coordinates and scores. +::: + +:::{grid-item-card} {octicon}`gear;1.1em` Configure the predictor +:link: ../user_guide/ocr_predictor +:link-type: doc + +Choose the models and tune the pipeline to your documents. +::: + +:::{grid-item-card} {octicon}`sync;1.1em` Rotated documents +:link: ../user_guide/rotated_documents +:link-type: doc + +Read skewed scans, photos and upside-down pages. +::: +:::: diff --git a/docs/source/getting_started/quickstart.rst b/docs/source/getting_started/quickstart.rst deleted file mode 100644 index 94818a2415..0000000000 --- a/docs/source/getting_started/quickstart.rst +++ /dev/null @@ -1,110 +0,0 @@ - -********** -Quickstart -********** - -This page shows you how to get OCR results from a document in just a few lines of code. -For more details see :ref:`using_models`. - - -Load a document -=============== - -docTR can read PDFs, images, and web pages: - -.. code:: python3 - - from doctr.io import DocumentFile - - # From a PDF - doc = DocumentFile.from_pdf("path/to/your/doc.pdf") - # From one or more images - doc = DocumentFile.from_images("path/to/your/img.jpg") - doc = DocumentFile.from_images(["path/to/page1.jpg", "path/to/page2.jpg"]) - # From a URL (requires the ``html`` extra: pip install "python-doctr[html]") - doc = DocumentFile.from_url("https://www.example.com") - - -Run OCR -======= - -.. code:: python3 - - from doctr.io import DocumentFile - from doctr.models import ocr_predictor - - doc = DocumentFile.from_pdf("path/to/your/doc.pdf") - model = ocr_predictor(pretrained=True) - result = model(doc) - -The predictor uses ``db_resnet50`` for text detection and ``crnn_vgg16_bn`` for text recognition by default. -You can choose any combination of :ref:`supported architectures `. - - -Inspect the output -================== - -The result is a :class:`~doctr.io.Document` object. - -Render as plain text:: - - print(result.render()) - -Export as a nested dictionary (JSON-serialisable):: - - import json - print(json.dumps(result.export(), indent=2)) - -Visualize on screen (requires the ``viz`` extra: ``pip install "python-doctr[viz]"``):: - - result.pages[0].show() - - -Multi-page PDF end-to-end example -================================== - -The following snippet processes every page of a PDF and collects the plain-text output: - -.. code:: python3 - - import json - from doctr.io import DocumentFile - from doctr.models import ocr_predictor - - model = ocr_predictor(pretrained=True) - doc = DocumentFile.from_pdf("path/to/multi_page.pdf") - result = model(doc) - - # Plain-text — one string per page - for page_idx, page in enumerate(result.pages): - print(f"--- Page {page_idx + 1} ---") - print(page.render()) - - # Structured output — JSON-serialisable dict - output = result.export() - with open("ocr_output.json", "w") as f: - json.dump(output, f, indent=2) - - -Common pitfalls -=============== - -.. note:: - - * **Visualization** requires the ``viz`` extra (installs ``matplotlib`` and ``mplcursors``): - ``pip install "python-doctr[viz]"``. Calls to ``result.show()`` or - ``result.pages[0].show()`` raise a ``ModuleNotFoundError`` without it. - * **HTML input** requires the ``html`` extra: ``pip install "python-doctr[html]"``. - * **Image format**: pass file paths or NumPy ``uint8`` arrays shaped ``(H, W, C)`` in - RGB order. Grayscale arrays must be converted to 3-channel before use. - * **Pretrained weights** are downloaded on first use and cached locally. Subsequent calls are instantaneous. - * **PDF pages are returned as images**: ``DocumentFile.from_pdf`` returns one - NumPy array per page, so ``result.pages[i]`` corresponds to the *i*-th PDF page. - - -Next steps -========== - -* :doc:`../using_doctr/using_models` - full predictor guide, architecture benchmarks, GPU usage. -* :doc:`../using_doctr/custom_models_training` - train and load your own models. -* :doc:`../using_doctr/sharing_models` - share your trained models on Hugging Face Hub. diff --git a/docs/source/index.md b/docs/source/index.md new file mode 100644 index 0000000000..cc61345889 --- /dev/null +++ b/docs/source/index.md @@ -0,0 +1,194 @@ +--- +myst: + html_meta: + description: "docTR: state-of-the-art OCR for PDFs and images in a few lines of Python. Text detection, text recognition, layout analysis and table structure recognition, powered by PyTorch." +--- + +# docTR: Document Text Recognition + +```{raw} html +
        +
        +
        +
        Open-source OCR · Powered by PyTorch
        +

        OCR made seamless & accessible to anyone

        +

        + docTR reads PDFs and images and gives you back every word with its position, organized into + lines, blocks, layout regions and tables. Run state-of-the-art models in three lines of Python, + fine-tune them on your own documents and ship them anywhere. +

        +
        + Get started → + User guide + GitHub +
        +
        + $ pip install python-doctr + +
        +
        +
        +
        ocr.py
        +
        from doctr.io import DocumentFile
+from doctr.models import ocr_predictor
+
+model = ocr_predictor(pretrained=True)
+doc = DocumentFile.from_pdf("invoice.pdf")
+result = model(doc)
+
+# or export to JSON, Markdown, HTML...
+print(result.render())
        +
        ACME Office Supplies + +INVOICE + +42 Harbour Road, Hamburg + +No. 2026-0412 +Date: 26/10/2026 +…
        +
        +
        +
        +
        4 tasksdetection · recognition · layout · tables
        +
        20+pretrained architectures
        +
        100+built-in vocabularies
        +
        6export formats
        +
        Apache 2.0free for commercial use
        +
        +
        +``` + +```{raw} html +

        Everything you need to read documents

        +

        From a scanned receipt to a hundred-page report: docTR covers the whole pipeline, and every stage can be swapped, tuned or retrained.

        +``` + +::::{grid} 1 2 3 3 +:gutter: 3 + +:::{grid-item-card} {octicon}`zap;1.1em` Two-stage OCR +:link: user_guide/ocr_predictor +:link-type: doc + +A text detection model finds the words, a recognition model reads them. Mix and match 9 detection and 9 recognition architectures. +::: + +:::{grid-item-card} {octicon}`table;1.1em` Layout & tables +:link: user_guide/layout_and_tables +:link-type: doc + +Detect titles, paragraphs, figures and tables, and turn tables into a row/column grid that loads straight into pandas. +::: + +:::{grid-item-card} {octicon}`sync;1.1em` Rotated documents +:link: user_guide/rotated_documents +:link-type: doc + +Handle skewed scans, upside-down pages and vertical text with built-in page and word orientation classifiers. +::: + +:::{grid-item-card} {octicon}`file-code;1.1em` Export anywhere +:link: user_guide/exporting +:link-type: doc + +Get plain text, JSON, Markdown, AsciiDoc, HTML or hOCR, in a reading order that follows columns, headers and footers. +::: + +:::{grid-item-card} {octicon}`mortar-board;1.1em` Train & share +:link: training/index +:link-type: doc + +Fine-tune any model on your own data with the reference training scripts, then share it on the Hugging Face Hub. +::: + +:::{grid-item-card} {octicon}`rocket;1.1em` Production ready +:link: deployment/index +:link-type: doc + +GPU & half precision, `torch.compile`, ONNX export, Docker images, a CLI and a REST API template. +::: +:::: + +```{raw} html +

        How it works

        +

        An OCR predictor chains specialized models. Each stage is a regular PyTorch module you can replace with your own.

        +``` + +```{raw} html +:file: _includes/pipeline.html +``` + +```{raw} html +

        Find your way around

        +

        Whether you need text out of a PDF in five minutes or want to train a model for a new alphabet, start here.

        +``` + +::::{grid} 1 2 3 3 +:gutter: 3 + +:::{grid-item-card} {octicon}`play;1.1em` Quickstart +:link: getting_started/quickstart +:link-type: doc + +Install docTR and extract the text of your first document in five minutes. +::: + +:::{grid-item-card} {octicon}`book;1.1em` User guide +:link: user_guide/index +:link-type: doc + +Load documents, configure the OCR predictor, navigate and export the results. +::: + +:::{grid-item-card} {octicon}`stack;1.1em` Model zoo +:link: models/index +:link-type: doc + +All architectures with their benchmarks, to pick the right speed/accuracy trade-off. +::: + +:::{grid-item-card} {octicon}`beaker;1.1em` Training +:link: training/index +:link-type: doc + +Datasets, data formats, training scripts and how to load your own checkpoints. +::: + +:::{grid-item-card} {octicon}`server;1.1em` Deployment +:link: deployment/index +:link-type: doc + +Speed up inference, export to ONNX, and serve docTR with Docker or a REST API. +::: + +:::{grid-item-card} {octicon}`code-square;1.1em` API reference +:link: api/index +:link-type: doc + +Every public class and function, generated from the source code. +::: +:::: + +```{raw} html +
        +
        +

        Need help with a complex use case?

        +

        docTR was created by Mindee and is developed and maintained by t2k GmbH, + who offer custom development, integration and consulting around docTR.

        +
        + Get support → +
        +``` + +```{toctree} +:hidden: + +getting_started/index +user_guide/index +models/index +training/index +deployment/index +api/index +community/index +``` diff --git a/docs/source/index.rst b/docs/source/index.rst deleted file mode 100644 index 1cfdb298b6..0000000000 --- a/docs/source/index.rst +++ /dev/null @@ -1,127 +0,0 @@ -******************************** -docTR: Document Text Recognition -******************************** - -State-of-the-art Optical Character Recognition made seamless & accessible to anyone, powered by PyTorch - -.. image:: https://github.com/mindee/doctr/releases/download/v0.2.0/ocr.png - :align: center - - -DocTR provides an easy and powerful way to extract valuable information from your documents: - -* |:receipt:| **for automation**: seamlessly process documents for Natural Language Understanding tasks: we provide OCR predictors to parse textual information (localize and identify each word) from your documents. -* |:woman_scientist:| **for research**: quickly compare your own architectures speed & performances with state-of-art models on public datasets. - - -Main Features -------------- - -* |:robot:| Robust 2-stage (detection + recognition) OCR predictors with pretrained parameters -* |:page_facing_up:| Layout analysis predictor for detecting document regions (tables, figures, headers, …) -* |:zap:| User-friendly, 3 lines of code to load a document and extract text with a predictor -* |:rocket:| State-of-the-art performance on public document datasets, comparable with GoogleVision/AWS Textract -* |:zap:| Optimized for inference speed on both CPU & GPU -* |:bird:| Light package, minimal dependencies -* |:tools:| Actively maintained by Mindee -* |:factory:| Easy integration (available templates for browser demo & API deployment) - - -.. toctree:: - :maxdepth: 2 - :caption: Getting started - :hidden: - - getting_started/installing - getting_started/quickstart - notebooks - - -Model zoo -^^^^^^^^^ - -Text detection models -""""""""""""""""""""" -* DBNet from `"Real-time Scene Text Detection with Differentiable Binarization" `_ -* LinkNet from `"LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation" `_ -* FAST from `"FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation" `_ - -Text recognition models -""""""""""""""""""""""" -* SAR from `"Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition" `_ -* CRNN from `"An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition" `_ -* MASTER from `"MASTER: Multi-Aspect Non-local Network for Scene Text Recognition" `_ -* ViTSTR from `"Vision Transformer for Fast and Efficient Scene Text Recognition" `_ -* PARSeq from `"Scene Text Recognition with Permuted Autoregressive Sequence Models" `_ -* VIPTR from `"A Vision Permutable Extractor for Fast and Efficient Scene Text Recognition" `_ - - -Supported datasets -^^^^^^^^^^^^^^^^^^ -* FUNSD from `"FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents" `_. -* CORD from `"CORD: A Consolidated Receipt Dataset forPost-OCR Parsing" `_. -* SROIE from `ICDAR 2019 `_. -* IIIT-5k from `CVIT `_. -* Street View Text from `"End-to-End Scene Text Recognition" `_. -* SynthText from `Visual Geometry Group `_. -* SVHN from `"Reading Digits in Natural Images with Unsupervised Feature Learning" `_. -* IC03 from `ICDAR 2003 `_. -* IC13 from `ICDAR 2013 `_. -* IMGUR5K from `"TextStyleBrush: Transfer of Text Aesthetics from a Single Example" `_. -* MJSynth from `"Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition" `_. -* IIITHWS from `"Generating Synthetic Data for Text Recognition" `_. -* WILDRECEIPT from `"Spatial Dual-Modality Graph Reasoning for Key Information Extraction" `_. -* COCO-Text dataset from `"COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images" `_. - -.. toctree:: - :maxdepth: 2 - :caption: Using docTR - :hidden: - - using_doctr/using_models - using_doctr/using_datasets - using_doctr/using_contrib_modules - using_doctr/sharing_models - using_doctr/using_model_export - using_doctr/custom_models_training - using_doctr/running_on_aws - using_doctr/using_cli - - -.. toctree:: - :maxdepth: 2 - :caption: Community - :hidden: - - community/resources - community/tools - - -.. toctree:: - :maxdepth: 2 - :caption: Package Reference - :hidden: - - modules/contrib - modules/datasets - modules/io - modules/models - modules/transforms - modules/utils - - -.. toctree:: - :maxdepth: 2 - :caption: Contributing - :hidden: - - contributing/code_of_conduct - contributing/contributing - - -.. toctree:: - :maxdepth: 2 - :caption: Notes - :hidden: - - changelog diff --git a/docs/source/models/detection.md b/docs/source/models/detection.md new file mode 100644 index 0000000000..10e1de1054 --- /dev/null +++ b/docs/source/models/detection.md @@ -0,0 +1,49 @@ +# Text detection + +Text detection models localize the words of a page. In docTR, a *word* is an uninterrupted sequence of characters. +All models predict a segmentation map that is turned into boxes, and support both straight and rotated boxes +(`assume_straight_pages=False`). + +## Architectures + +| Family | Paper | Variants | +| --- | --- | --- | +| **DBNet** | [Real-time Scene Text Detection with Differentiable Binarization](https://arxiv.org/pdf/1911.08947.pdf) | {func}`~doctr.models.detection.db_resnet34`, {func}`~doctr.models.detection.db_resnet50`, {func}`~doctr.models.detection.db_mobilenet_v3_large` | +| **LinkNet** | [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://arxiv.org/pdf/1707.03718.pdf) | {func}`~doctr.models.detection.linknet_resnet18`, {func}`~doctr.models.detection.linknet_resnet34`, {func}`~doctr.models.detection.linknet_resnet50` | +| **FAST** | [FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation](https://arxiv.org/pdf/2111.02394.pdf) | {func}`~doctr.models.detection.fast_tiny`, {func}`~doctr.models.detection.fast_small`, {func}`~doctr.models.detection.fast_base` {bdg-secondary}`default` | + +## Benchmark + +Recall and precision on FUNSD and CORD, and latency with a batch size of 1: + +| Architecture | Input shape | # params | FUNSD recall | FUNSD precision | CORD recall | CORD precision | sec/it | +| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | +| `db_resnet34` | (1024, 1024, 3) | 22.4 M | 82.76 | 76.75 | 89.20 | 71.74 | 0.8 | +| `db_resnet50` | (1024, 1024, 3) | 25.4 M | 83.56 | 86.68 | 92.61 | 86.39 | 1.1 | +| `db_mobilenet_v3_large` | (1024, 1024, 3) | 4.2 M | 82.69 | 84.63 | **94.51** | 70.28 | 0.5 | +| `linknet_resnet18` | (1024, 1024, 3) | 11.5 M | 81.64 | 85.52 | 88.92 | 82.74 | 0.6 | +| `linknet_resnet34` | (1024, 1024, 3) | 21.6 M | 81.62 | 82.95 | 86.26 | 81.06 | 0.7 | +| `linknet_resnet50` | (1024, 1024, 3) | 28.8 M | 81.78 | 82.47 | 87.29 | 85.54 | 1.0 | +| `fast_tiny` | (1024, 1024, 3) | 13.5 M (8.5 M) | 84.90 | 85.04 | 93.73 | 76.26 | 0.7 (0.4) | +| `fast_small` | (1024, 1024, 3) | 14.7 M (9.7 M) | **85.36** | 86.68 | 94.09 | 78.53 | 0.7 (0.5) | +| `fast_base` | (1024, 1024, 3) | 16.3 M (10.6 M) | 84.95 | **86.73** | 94.39 | 85.36 | 0.8 (0.5) | + +:::{note} +FAST models are **reparameterized** by `detection_predictor` and `ocr_predictor`: their branches are fused into +single convolutions for inference. Values in parentheses are measured after reparameterization. +::: + +## Usage + +```python +from doctr.models import ocr_predictor, detection_predictor + +# In the OCR pipeline +predictor = ocr_predictor(det_arch="db_resnet50", pretrained=True) + +# On its own +det_predictor = detection_predictor("db_resnet50", pretrained=True) +``` + +Tune the binarization and box thresholds of the post-processor to detect more (or fewer) words, see +[](../user_guide/ocr_predictor.md#tune-the-text-detection). diff --git a/docs/source/models/end_to_end.md b/docs/source/models/end_to_end.md new file mode 100644 index 0000000000..57c6423ac4 --- /dev/null +++ b/docs/source/models/end_to_end.md @@ -0,0 +1,31 @@ +# End-to-end OCR + +An OCR predictor combines a detection model with a recognition model: any pair of the architectures listed in this +section works. A word counts as correct when it is both well localized and correctly transcribed. + +## Benchmark + +| Architecture | FUNSD recall | FUNSD precision | CORD recall | CORD precision | +| --- | ---: | ---: | ---: | ---: | +| `db_resnet50` + `crnn_vgg16_bn` | 73.37 | 76.11 | 84.80 | 79.09 | +| `db_resnet50` + `crnn_mobilenet_v3_small` | 73.06 | 75.79 | 84.64 | 78.94 | +| `db_resnet50` + `crnn_mobilenet_v3_large` | 73.17 | 75.90 | 84.96 | 79.25 | +| `db_resnet50` + `master` | **73.90** | **76.66** | 85.84 | 80.07 | +| `db_resnet50` + `sar_resnet31` | 73.58 | 76.33 | 85.64 | 79.88 | +| `db_resnet50` + `vitstr_small` | 73.06 | 75.79 | **85.95** | **80.17** | +| `db_resnet50` + `vitstr_base` | 73.70 | 76.46 | 85.76 | 79.99 | +| `db_resnet50` + `parseq` | 73.52 | 76.27 | 85.91 | 80.13 | +| *Google Vision — text detection* | 59.50 | 62.50 | 75.30 | 59.03 | +| *Google Vision — document text detection* | 64.00 | 53.30 | 68.90 | 61.10 | +| *AWS Textract* | 78.10 | 83.00 | 87.50 | 66.00 | +| *Azure Form Recognizer (v3.2)* | 79.42 | 85.89 | 89.62 | 88.93 | + +Commercial services are shown in italics, for reference. + +## Usage + +```python +from doctr.models import ocr_predictor + +predictor = ocr_predictor(det_arch="db_resnet50", reco_arch="master", pretrained=True) +``` diff --git a/docs/source/models/index.md b/docs/source/models/index.md new file mode 100644 index 0000000000..8599be215e --- /dev/null +++ b/docs/source/models/index.md @@ -0,0 +1,71 @@ +# Model zoo + +docTR implements a range of published architectures for each task of the pipeline, all with pretrained weights. +This section lists them with their benchmarks, so you can pick the right trade-off between speed and accuracy. + +## At a glance + +| Task | Architectures | Default | Use it with | +| --- | --- | --- | --- | +| [Text detection](detection.md) | DBNet, LinkNet, FAST | `fast_base` | `ocr_predictor(det_arch=...)` | +| [Text recognition](recognition.md) | CRNN, SAR, MASTER, ViTSTR, PARSeq, VIPTR | `crnn_vgg16_bn` | `ocr_predictor(reco_arch=...)` | +| [Layout analysis](layout.md) | LW-DETR | `lw_detr_s` | `ocr_predictor(detect_layout=True, layout_arch=...)` | +| [Table structure](table_structure.md) | TableCenterNet | `tablecenternet` | `ocr_predictor(detect_tables=True)` | +| [Orientation](#orientation-classifiers) | MobileNetV3 | `mobilenet_v3_small_*_orientation` | `ocr_predictor(assume_straight_pages=False)` | + +[End-to-end benchmarks](end_to_end.md) compare detection + recognition pairs, including commercial OCR services. + +## How to choose + +::::{grid} 1 1 3 3 +:gutter: 3 + +:::{grid-item-card} {octicon}`check-circle;1.1em` Balanced +The defaults, `fast_base` + `crnn_vgg16_bn`, are accurate on most documents and run fine on a CPU. +::: + +:::{grid-item-card} {octicon}`zap;1.1em` Fast +`fast_tiny` or `db_mobilenet_v3_large` with `crnn_mobilenet_v3_small` or `viptr_tiny` for CPUs, edge devices and +high-volume pipelines. +::: + +:::{grid-item-card} {octicon}`goal;1.1em` Accurate +`db_resnet50` or `fast_base` with `parseq`, `vitstr_base` or `master`, ideally on a GPU. +::: +:::: + +Benchmarks on public datasets are a starting point: the best way to choose is to compare a few combinations on a +sample of **your** documents. If none is good enough, [fine-tune a model](../training/index.md) on your data. + +## Orientation classifiers + +Two lightweight MobileNetV3 classifiers handle [rotated documents](../user_guide/rotated_documents.md). They are +loaded automatically when needed. + +| Architecture | Predicts | Factory | +| ---------------------------------------- | ------------------------------------------------ | ---------------------------------------------------------- | +| `mobilenet_v3_small_page_orientation` | The orientation of a page: 0°, 90°, 180°, 270° | {func}`~doctr.models.classification.page_orientation_predictor` | +| `mobilenet_v3_small_crop_orientation` | The orientation of a word crop: 0°, 90°, 180°, 270° | {func}`~doctr.models.classification.crop_orientation_predictor` | + +## About the benchmarks + +- **Datasets**: unless stated otherwise, models are evaluated on both the training and test sets of + [FUNSD](https://guillaumejaume.github.io/FUNSD/) and [CORD](https://github.com/clovaai/cord) (see + [](../training/datasets.md)). FUNSD has 199 pages (30,595 word crops), which might not be representative of every + use case. +- **Metrics** are described in [](../api/utils.md#task-evaluation). +- **Speed** (`sec/it`) is measured on an *11th Gen Intel(R) Core(TM) i7-11800H @ 2.30GHz* CPU, after a warm-up of + 100 tensors, as the average over 1,000 samples. The batch size is given in each table. +- Reproduce or extend them with the `evaluate.py` and `latency.py` scripts of the + [references](https://github.com/mindee/doctr/tree/main/references). + +```{toctree} +:hidden: + +detection +recognition +layout +table_structure +end_to_end +vocabs +``` diff --git a/docs/source/models/layout.md b/docs/source/models/layout.md new file mode 100644 index 0000000000..24e3d5325b --- /dev/null +++ b/docs/source/models/layout.md @@ -0,0 +1,36 @@ +# Layout analysis + +Layout models localize and classify the regions of a page (titles, paragraphs, tables, figures, headers...). Like +the text detection models, they work on rotated and skewed documents. + +## Architectures + +| Architecture | Paper | Pretrained weights | +| --- | --- | --- | +| {func}`~doctr.models.layout.lw_detr_s` {bdg-secondary}`default` | [LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection](https://arxiv.org/pdf/2406.03459v1) | {bdg-success}`available` | +| {func}`~doctr.models.layout.lw_detr_m` | Same, larger backbone | {bdg-warning}`coming soon` | + +The pretrained model predicts 11 classes: `Caption`, `Footnote`, `Formula`, `List-item`, `Page-footer`, +`Page-header`, `Picture`, `Section-header`, `Table`, `Text` and `Title`. + +## Benchmark + +| Architecture | Input shape | # params | mAP@[.5:.95] | AP@.5 | AP@.75 | sec/it (batch 1) | +| --- | --- | ---: | ---: | ---: | ---: | ---: | +| `lw_detr_s` | (1024, 1024, 3) | 15.1 M | 66.89 | 82.83 | 73.75 | 0.5 | +| `lw_detr_m` | (1024, 1024, 3) | 29.5 M | soon | soon | soon | 0.7 | + +## Usage + +```python +from doctr.models import layout_predictor, ocr_predictor + +# In the OCR pipeline +predictor = ocr_predictor(pretrained=True, detect_layout=True, layout_arch="lw_detr_s") + +# On its own +layout = layout_predictor("lw_detr_s", pretrained=True) +``` + +See [](../user_guide/layout_and_tables.md) for how the regions are used, and +[](../training/custom_models.md#layout-models) to plug a model trained on your own classes. diff --git a/docs/source/models/recognition.md b/docs/source/models/recognition.md new file mode 100644 index 0000000000..6d705fe593 --- /dev/null +++ b/docs/source/models/recognition.md @@ -0,0 +1,55 @@ +# Text recognition + +Text recognition models transcribe the characters of a word crop. They output a string and a confidence score, and +can only predict the characters of the [vocabulary](vocabs.md) they were trained on. + +## Architectures + +| Architecture | Paper | Decoding | +| --- | --- | --- | +| {func}`~doctr.models.recognition.crnn_vgg16_bn` {bdg-secondary}`default`, {func}`~doctr.models.recognition.crnn_mobilenet_v3_small`, {func}`~doctr.models.recognition.crnn_mobilenet_v3_large` | [An End-to-End Trainable Neural Network for Image-based Sequence Recognition](https://arxiv.org/pdf/1507.05717.pdf) | CTC | +| {func}`~doctr.models.recognition.sar_resnet31` | [Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition](https://arxiv.org/pdf/1811.00751.pdf) | Attention | +| {func}`~doctr.models.recognition.master` | [MASTER: Multi-Aspect Non-local Network for Scene Text Recognition](https://arxiv.org/pdf/1910.02562.pdf) | Transformer | +| {func}`~doctr.models.recognition.vitstr_small`, {func}`~doctr.models.recognition.vitstr_base` | [Vision Transformer for Fast and Efficient Scene Text Recognition](https://arxiv.org/pdf/2105.08582.pdf) | Parallel | +| {func}`~doctr.models.recognition.parseq` | [Scene Text Recognition with Permuted Autoregressive Sequence Models](https://arxiv.org/pdf/2207.06966) | Permuted autoregressive | +| {func}`~doctr.models.recognition.viptr_tiny` | [A Vision Permutable Extractor for Fast and Efficient Scene Text Recognition](https://arxiv.org/abs/2401.10110) | CTC | + +## Benchmark + +Word accuracy on FUNSD and CORD word crops (exact and partial match, see {class}`~doctr.utils.metrics.TextMatch`), and latency with a batch size of 64: + +| Architecture | Input shape | # params | FUNSD exact | FUNSD partial | CORD exact | CORD partial | sec/it | +| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | +| `crnn_vgg16_bn` | (32, 128, 3) | 15.8 M | 88.21 | 88.95 | 95.47 | 95.91 | 0.6 | +| `crnn_mobilenet_v3_small` | (32, 128, 3) | 2.1 M | 87.25 | 87.99 | 93.91 | 94.34 | **0.05** | +| `crnn_mobilenet_v3_large` | (32, 128, 3) | 4.5 M | 87.38 | 88.09 | 94.46 | 94.92 | 0.08 | +| `master` | (32, 128, 3) | 58.7 M | **88.57** | **89.39** | **95.73** | **96.21** | 17.6 | +| `sar_resnet31` | (32, 128, 3) | 55.4 M | 88.10 | 88.88 | 94.83 | 95.29 | 4.9 | +| `vitstr_small` | (32, 128, 3) | 21.4 M | 88.00 | 88.82 | 95.40 | 95.78 | 1.5 | +| `vitstr_base` | (32, 128, 3) | 85.2 M | 88.33 | 89.09 | 95.32 | 95.71 | 4.1 | +| `parseq` | (32, 128, 3) | 23.8 M | 88.53 | 89.24 | 95.56 | 95.91 | 2.2 | +| `viptr_tiny` | (32, 128, 3) | 3.2 M | 86.03 | 86.71 | 93.08 | 93.47 | 0.08 | + +## Usage + +```python +from doctr.models import ocr_predictor, recognition_predictor + +# In the OCR pipeline +predictor = ocr_predictor(reco_arch="parseq", pretrained=True) + +# On its own, on word crops +reco_predictor = recognition_predictor("parseq", pretrained=True) + +# The characters the model can predict +print(reco_predictor.model.cfg["vocab"]) +``` + +## Languages + +The pretrained recognition models are trained on the `french` vocabulary: Latin characters with the accents of +the main Western European languages, digits, punctuation and common currency symbols. For other languages: + +- restrict the output to the characters you expect with a [whitelist](../user_guide/ocr_predictor.md#restrict-the-output-characters); +- look for a [community model](../training/sharing_models.md#community-models), such as the multilingual PARSeq; +- [train your own](../training/train.md) on one of the [100+ vocabularies](vocabs.md). diff --git a/docs/source/models/table_structure.md b/docs/source/models/table_structure.md new file mode 100644 index 0000000000..fe95454479 --- /dev/null +++ b/docs/source/models/table_structure.md @@ -0,0 +1,34 @@ +# Table structure recognition + +Table structure models parse the image of a table into a machine-understandable grid: they localize every cell +(spatial structure) and recover the rows and columns it spans (logical structure). + +## Architectures + +| Architecture | Paper | +| --- | --- | +| {func}`~doctr.models.table_structure.tablecenternet` {bdg-secondary}`default` | [TableCenterNet: A one-stage network for table structure recognition](https://arxiv.org/abs/2504.17522) | + +## Benchmark + +| Architecture | Input shape | # params | Recall | Precision | F1 | Structure acc. | sec/it (batch 1) | +| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | +| `tablecenternet` | (1024, 1024, 3) | 7.1 M | 82.31 | 96.01 | 88.64 | 77.53 | 0.5 | + +The metrics are produced by `references/table/evaluate.py` with +{class}`~doctr.utils.metrics.TableCellMetric`: cell detection **recall**, **precision** and **F1** (cells matched +above an IoU of 0.5), and **structure accuracy**, the share of matched cells whose row and column spans are correct. + +## Usage + +```python +from doctr.models import ocr_predictor, table_predictor + +# In the OCR pipeline: tables are located by the layout model, then parsed +predictor = ocr_predictor(pretrained=True, detect_tables=True) + +# On its own, on images of tables +tables = table_predictor("tablecenternet", pretrained=True) +``` + +See [](../user_guide/layout_and_tables.md#table-structure-recognition) to work with the recognized tables. diff --git a/docs/source/models/vocabs.md b/docs/source/models/vocabs.md new file mode 100644 index 0000000000..13d300ff31 --- /dev/null +++ b/docs/source/models/vocabs.md @@ -0,0 +1,34 @@ +# Vocabularies + +A vocabulary is the set of characters a recognition model can predict. docTR ships more than 100 of them, covering +the main writing systems, in {data}`doctr.datasets.VOCABS`: + +```python +from doctr.datasets import VOCABS + +print(len(VOCABS["german"])) # 108 +print(VOCABS["german"][-8:]) # äöüßÄÖÜẞ +``` + +They are used to: + +- **train** a recognition model on a language: `crnn_vgg16_bn(vocab=VOCABS["german"])`, or + `--vocab german` with the [training script](../training/train.md#text-recognition); +- **restrict** a trained model to a subset of its characters with a + [whitelist](../user_guide/ocr_predictor.md#restrict-the-output-characters). + +:::{tip} +Vocabularies are plain strings, so you can combine them: `VOCABS["german"] + VOCABS["polish"]`. Remove the +duplicates while keeping the order with `"".join(dict.fromkeys(...))`. +::: + +## Available vocabularies + +Click a row to see all its characters. For Latin-script languages, the summary lists the characters added to the +`english` vocabulary. + +```{include} _generated/vocabs.md +``` + +`VOCABS` also contains the building blocks these vocabularies are made of, such as `digits`, `punctuation`, +`currency` or the consonants and vowels of each Indic script. diff --git a/docs/source/modules/contrib.rst b/docs/source/modules/contrib.rst deleted file mode 100644 index 1a2659e2e3..0000000000 --- a/docs/source/modules/contrib.rst +++ /dev/null @@ -1,13 +0,0 @@ -doctr.contrib -============= - -.. currentmodule:: doctr.contrib - -This module contains all the available contribution modules for docTR. - - -Supported contribution modules ------------------------------- -Here are all the available contribution modules: - -.. autoclass:: ArtefactDetector diff --git a/docs/source/modules/datasets.rst b/docs/source/modules/datasets.rst deleted file mode 100644 index 234e280903..0000000000 --- a/docs/source/modules/datasets.rst +++ /dev/null @@ -1,404 +0,0 @@ -doctr.datasets -============== - -.. currentmodule:: doctr.datasets - -.. _datasets: - -doctr.datasets --------------- - -.. autoclass:: FUNSD - -.. autoclass:: SROIE - -.. autoclass:: CORD - -.. autoclass:: IIIT5K - -.. autoclass:: SVT - -.. autoclass:: SVHN - -.. autoclass:: SynthText - -.. autoclass:: IC03 - -.. autoclass:: IC13 - -.. autoclass:: IMGUR5K - -.. autoclass:: MJSynth - -.. autoclass:: IIITHWS - -.. autoclass:: DocArtefacts - -.. autoclass:: WILDRECEIPT - -.. autoclass:: COCOTEXT - -Synthetic dataset generator ---------------------------- - -.. autoclass:: CharacterGenerator - -.. autoclass:: WordGenerator - -Custom dataset loader ---------------------- - -.. autoclass:: DetectionDataset - -.. autoclass:: LayoutDataset - -.. autoclass:: RecognitionDataset - -.. autoclass:: TableStructureDataset - -.. autoclass:: OCRDataset - -Dataset utils -------------- - -.. autofunction:: translate - -.. autofunction:: encode_string - -.. autofunction:: decode_sequence - -.. autofunction:: encode_sequences - -.. autofunction:: pre_transform_multiclass - -.. autofunction:: crop_bboxes_from_image - -.. autofunction:: convert_target_to_relative - -.. _vocabs: - -Supported Vocabs ----------------- - -Since textual content has to be encoded properly for models to interpret them efficiently, docTR supports multiple sets -of vocabs. - -.. list-table:: docTR Vocabs - :widths: 20 5 50 - :header-rows: 1 - - * - Name - - size - - characters - * - latex - - 81 - - ()+,-./0123456789<=>ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_abcdefghijklmnopqrstuvwxyz{|} - * - latin - - 94 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ - * - english - - 100 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ - * - albanian - - 104 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿çëÇË - * - afrikaans - - 128 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿èëïîôûêÈËÏÎÔÛÊáéíóúäöÁÉÍÓÚÄÖ - * - azerbaijani - - 113 - - 0123456789abcdefghijklmnopqrstuvxyzABCDEFGHIJKLMNOPQRSTUVXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿çəğıöşüÇƏĞİÖŞÜ₼ - * - basque - - 104 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ñçÑÇ - * - bosnian - - 102 - - 0123456789abcdefghijklmnoprstuvzABCDEFGHIJKLMNOPRSTUVZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿čćđšžČĆĐŠŽ - * - catalan - - 123 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿àèéíïòóúüçÀÈÉÍÏÒÓÚÜÇ·ŀĿ - * - croatian - - 110 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ČčĆćĐ𩹮ž - * - czech - - 130 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿áčďéěíňóřšťúůýžÁČĎÉĚÍŇÓŘŠŤÚŮÝŽ - * - danish - - 106 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿æøåÆØÅ - * - dutch - - 126 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿áéíóúüñÁÉÍÓÚÜÑäëïöèêÄËÏÖÈÊ - * - estonian - - 112 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿šžõäöüŠŽÕÄÖÜ - * - esperanto - - 105 - - 0123456789abcdefghijklmnoprstuvzABCDEFGHIJKLMNOPRSTUVZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ĉĝĥĵŝŭĈĜĤĴŜŬ₷ - * - french - - 126 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿àâéèêëîïôùûüçÀÂÉÈÊËÎÏÔÙÛÜÇ - * - finnish - - 104 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿äöÄÖ - * - frisian - - 108 - - 0123456789abcdefghijklmnoprstuvwyzABCDEFGHIJKLMNOPRSTUVWYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿âêôûúÂÊÔÛÚƒƑ - * - galician - - 110 - - 0123456789abcdefghilmnopqrstuvxyzABCDEFGHILMNOPQRSTUVXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ñÑçÇáéíóúüÁÉÍÓÚÜ - * - german - - 108 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿äöüßÄÖÜẞ - * - hausa - - 101 - - 0123456789abcdefghijklmnorstuwyzABCDEFGHIJKLMNORSTUWYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ɓɗƙƴƁƊƘƳ₦ - * - hungarian - - 118 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿áéíóöúüÁÉÍÓÖÚÜőűŐŰ - * - icelandic - - 114 - - 0123456789abdefghijklmnoprstuvxyzABDEFGHIJKLMNOPRSTUVXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ðáéíóúýþæöÐÁÉÍÓÚÝÞÆÖ - * - indonesian - - 100 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ - * - irish - - 110 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿áéíóúÁÉÍÓÚ - * - italian - - 120 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿àèéìíîòóùúÀÈÉÌÍÎÒÓÙÚ - * - latvian - - 116 - - 0123456789abcdefghijklmnoprstuvyzABCDEFGHIJKLMNOPRSTUVYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿āčēģīķļņšūžĀČĒĢĪĶĻŅŠŪŽ - * - lithuanian - - 112 - - 0123456789abcdefghijklmnoprstuvyzABCDEFGHIJKLMNOPRSTUVYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ąčęėįšųūžĄČĘĖĮŠŲŪŽ - * - luxembourgish - - 110 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿äöüéëÄÖÜÉË - * - malagasy - - 94 - - 0123456789abdefghijklmnoprstvyzABDEFGHIJKLMNOPRSTVYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ôñÔÑ - * - malay - - 100 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ - * - maltese - - 114 - - 0123456789abdefghijklmnopqrstuvwxzABDEFGHIJKLMNOPQRSTUVWXZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ċġħżĊĠĦŻàèìòùÀÈÌÒÙ - * - maori - - 86 - - 0123456789aeghikmnoprtuwAEGHIKMNOPRTUW!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿āēīōūĀĒĪŌŪ - * - montenegrin - - 104 - - 0123456789abcdefghijklmnoprstuvzABCDEFGHIJKLMNOPRSTUVZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿čćšśžźČĆŠŚŽŹ - * - norwegian - - 106 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿æøåÆØÅ - * - polish - - 118 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ąćęłńóśźżĄĆĘŁŃÓŚŹŻ - * - portuguese - - 128 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿áàâãéêíïóôõúüçÁÀÂÃÉÊÍÏÓÔÕÚÜÇ - * - quechua - - 90 - - 0123456789acehiklmnopqrstuwyACEHIKLMNOPQRSTUWY!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ñÑĉĈçÇ - * - romanian - - 110 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ăâîșțĂÂÎȘȚ - * - scottish_gaelic - - 94 - - 0123456789abcdefghilmnoprstuABCDEFGHILMNOPRSTU!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿àèìòùÀÈÌÒÙ - * - serbian_latin - - 110 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿čćđžšČĆĐŽŠ - * - slovak - - 134 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ôäčďľňšťžáéíĺóŕúýÔÄČĎĽŇŠŤŽÁÉÍĹÓŔÚÝ - * - slovene - - 102 - - 0123456789abcdefghijklmnoprstuvzABCDEFGHIJKLMNOPRSTUVZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿čćđšžČĆĐŠŽ - * - somali - - 94 - - 0123456789abcdefghijklmnoqrstuwxyABCDEFGHIJKLMNOQRSTUWXY!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ - * - spanish - - 116 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿áéíóúüñÁÉÍÓÚÜÑ¡¿ - * - swahili - - 96 - - 0123456789abcdefghijklmnoprstuvwyzABCDEFGHIJKLMNOPRSTUVWYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ - * - swedish - - 106 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿åäöÅÄÖ - * - tagalog - - 103 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ñÑ₱ - * - turkish - - 113 - - 0123456789abcdefghijklmnoprstuvyzABCDEFGHIJKLMNOPRSTUVYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿çğıöşüâîûÇĞİÖŞÜÂÎÛ₺ - * - uzbek_latin - - 102 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ʻʼ - * - vietnamese - - 235 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿áàảạãăắằẳẵặâấầẩẫậđéèẻẽẹêếềểễệóòỏõọôốồổộỗơớờởợỡúùủũụưứừửữựíìỉĩịýỳỷỹỵÁÀẢẠÃĂẮẰẲẴẶÂẤẦẨẪẬĐÉÈẺẼẸÊẾỀỂỄỆÓÒỎÕỌÔỐỒỔỘỖƠỚỜỞỢỠÚÙỦŨỤƯỨỪỬỮỰÍÌỈĨỊÝỲỶỸỴ₫ - * - welsh - - 102 - - 0123456789abcdefghijlmnoprstuwyABCDEFGHIJLMNOPRSTUWY!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿âêîôŵŷÂÊÎÔŴŶ - * - yoruba - - 121 - - 0123456789abdefghijklmnoprstuwyABDEFGHIJKLMNOPRSTUWY!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ẹọṣẸỌṢàáèéìíòóùúÀÁÈÉÌÍÒÓÙÚńǹŃǸ₦ - * - zulu - - 100 - - 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~°£€¥¢฿ - * - russian - - 114 - - абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~£€¥¢฿₽ - * - belarusian - - 116 - - абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭ0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~£€¥¢฿ўіЎІ₽ - * - ukrainian - - 114 - - абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯ0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~£€¥¢฿ґіїєҐІЇЄ₴ - * - tatar - - 126 - - абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~£€¥¢฿₽ӘәҖҗҢңӨөҮүҺһ - * - tajik - - 125 - - абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~£€¥¢฿ҒғҚқҲҳҶҷӢӣӮӯ - * - kazakh - - 132 - - абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~£€¥¢฿ӘәҒғҚқҢңӨөҰұҮүҺһІі₸ - * - kyrgyz - - 119 - - абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~£€¥¢฿ҢңӨөҮү - * - bulgarian - - 107 - - абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯъЪ0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~£€¥¢฿ - * - macedonian - - 119 - - абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯ0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~£€¥¢฿ЃѓЅѕЈјЉљЊњЌќЏџ - * - mongolian - - 128 - - абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~£€¥¢฿ӨөҮү᠐᠑᠒᠓᠔᠕᠖᠗᠘᠙₮ - * - yakut - - 124 - - абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~£€¥¢฿ҔҕҤҥӨөҺһҮү₽ - * - serbian_cyrillic - - 107 - - абвгдежзиклмнопрстуфхцчшАБВГДЕЖЗИКЛМНОПРСТУФХЦЧШјЈЂђЉљЊњЋћЏџ0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~£€¥¢฿ - * - uzbek_cyrillic - - 121 - - абвгдежзийклмнопрстуфхцчшщьюяАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЬЮЯёыэЁЫЭъЪ0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~£€¥¢฿ЎўҚқҒғҲҳ - * - greek - - 117 - - 0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~αβγδεζηθικλμνξοπρστςυφχψωΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΤΥΦΧΨΩ£€¥¢฿άέήίϊΐόύϋΰώΆΈΉΊΪΌΎΫΏ· - * - greek_extended - - 312 - - 0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~αβγδεζηθικλμνξοπρστςυφχψωΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΤΥΦΧΨΩ£€¥¢฿άέήίϊΐόύϋΰώΆΈΉΊΪΌΎΫΏ·ͶͷϜϝἀἁἂἃἄἅἆἇἈἉἊἋἌἍἎἏἐἑἒἓἔἕἘἙἚἛἜἝἠἡἢἣἤἥἦἧἨἩἪἫἬἭἮἯἰἱἲἳἴἵἶἷἸἹἺἻἼἽἾἿὀὁὂὃὄὅὈὉὊὋὌὍὐὑὒὓὔὕὖὗὙὛὝὟὠὡὢὣὤὥὦὧὨὩὪὫὬὭὮὯὰὲὴὶὸὺὼᾀᾁᾂᾃᾄᾅᾆᾇᾈᾉᾊᾋᾌᾍᾎᾏᾐᾑᾒᾓᾔᾕᾖᾗᾘᾙᾚᾛᾜᾝᾞᾟᾠᾡᾢᾣᾤᾥᾦᾧᾨᾩᾪᾫᾬᾭᾮᾯᾲᾳᾴᾶᾷᾺᾼῂῃῄῆῇῈῊῌῒΐῖῗῚῢΰῤῥῦῧῪῬῲῳῴῶῷῸῺῼ - * - hebrew - - 176 - - 0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~אבגדהוזחטיךכלםמןנסעףפץצקרשתְֱֲֳִֵֶַָׇֹֺֻֽ־ֿ׀ׁׂ׃ׅׄ׆׳״֑֖֛֢֣֤֥֦֧֪֚֭֮֒֓֔֕֗֘֙֜֝֞֟֠֡֨֩֫֬֯ׯװױײיִﬞײַﬠﬡﬢﬣﬤﬥﬦﬧﬨ﬩שׁשׂשּׁשּׂאַאָאּבּגּדּהּוּזּטּיּךּכּלּמּנּסּףּפּצּקּרּשּתּוֹבֿכֿפֿﭏ₪ - * - arabic - - 116 - - 0123456789٠١٢٣٤٥٦٧٨٩ءآأؤإئابةتثجحخدذرزسشصضطظعغـفقكلمنهوىيٱپچژڢڤگکیًٌٍَُِّْٕٓٔٚ؟؛«»—،!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ - * - persian - - 126 - - 0123456789٠١٢٣٤٥٦٧٨٩ءآأؤإئابةتثجحخدذرزسشصضطظعغـفقكلمنهوىيٱپچژڢڤگکیًٌٍَُِّْٕٓٔٚ؟؛«»—،!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~۰۱۲۳۴۵۶۷۸۹ - * - urdu - - 134 - - 0123456789٠١٢٣٤٥٦٧٨٩ءآأؤإئابةتثجحخدذرزسشصضطظعغـفقكلمنهوىيٱپچژڢڤگکیًٌٍَُِّْٕٓٔٚ؟؛«»—،!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~۰۱۲۳۴۵۶۷۸۹ٹڈڑںھےہۃ - * - pashto - - 137 - - 0123456789٠١٢٣٤٥٦٧٨٩ءآأؤإئابةتثجحخدذرزسشصضطظعغـفقكلمنهوىيٱپچژڢڤگکیًٌٍَُِّْٕٓٔٚ؟؛«»—،!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~۰۱۲۳۴۵۶۷۸۹ټډړږښځڅڼېۍګ - * - kurdish - - 131 - - 0123456789٠١٢٣٤٥٦٧٨٩ءآأؤإئابةتثجحخدذرزسشصضطظعغـفقكلمنهوىيٱپچژڢڤگکیًٌٍَُِّْٕٓٔٚ؟؛«»—،!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~۰۱۲۳۴۵۶۷۸۹ڵڕۆێە - * - uyghur - - 134 - - 0123456789٠١٢٣٤٥٦٧٨٩ءآأؤإئابةتثجحخدذرزسشصضطظعغـفقكلمنهوىيٱپچژڢڤگکیًٌٍَُِّْٕٓٔٚ؟؛«»—،!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~۰۱۲۳۴۵۶۷۸۹ەېۆۇۈڭھۋ - * - sindhi - - 147 - - 0123456789٠١٢٣٤٥٦٧٨٩ءآأؤإئابةتثجحخدذرزسشصضطظعغـفقكلمنهوىيٱپچژڢڤگکیًٌٍَُِّْٕٓٔٚ؟؛«»—،!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~۰۱۲۳۴۵۶۷۸۹ڀٿٺٽڦڄڃڇڏڌڊڍڙڳڱڻھڪٻ۽۾ - * - devanagari - - 161 - - 0123456789कखगघङचछजझञटठडढणतथदधनपफबभमयरलवशषसहऴऩळक़ख़ग़ज़ड़ढ़फ़य़ऱॺॻॼॽॾअआइईउऊऋऌऍऎएऐऑऒओऔॠॡॲऄॵॶॳॴॷॸॹ०१२३४५६७८९़ंँः॒॑ािीुूृॄॅॆेैॉॊोौॢॣॏॎ्।॥॰ऽꣲ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~₹ - * - hindi - - 161 - - 0123456789कखगघङचछजझञटठडढणतथदधनपफबभमयरलवशषसहऴऩळक़ख़ग़ज़ड़ढ़फ़य़ऱॺॻॼॽॾअआइईउऊऋऌऍऎएऐऑऒओऔॠॡॲऄॵॶॳॴॷॸॹ०१२३४५६७८९़ंँः॒॑ािीुूृॄॅॆेैॉॊोौॢॣॏॎ्।॥॰ऽꣲ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~₹ - * - sanskrit - - 161 - - 0123456789कखगघङचछजझञटठडढणतथदधनपफबभमयरलवशषसहऴऩळक़ख़ग़ज़ड़ढ़फ़य़ऱॺॻॼॽॾअआइईउऊऋऌऍऎएऐऑऒओऔॠॡॲऄॵॶॳॴॷॸॹ०१२३४५६७८९़ंँः॒॑ािीुूृॄॅॆेैॉॊोौॢॣॏॎ्।॥॰ऽꣲ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~₹ - * - marathi - - 161 - - 0123456789कखगघङचछजझञटठडढणतथदधनपफबभमयरलवशषसहऴऩळक़ख़ग़ज़ड़ढ़फ़य़ऱॺॻॼॽॾअआइईउऊऋऌऍऎएऐऑऒओऔॠॡॲऄॵॶॳॴॷॸॹ०१२३४५६७८९़ंँः॒॑ािीुूृॄॅॆेैॉॊोौॢॣॏॎ्।॥॰ऽꣲ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~₹ - * - nepali - - 161 - - 0123456789कखगघङचछजझञटठडढणतथदधनपफबभमयरलवशषसहऴऩळक़ख़ग़ज़ड़ढ़फ़य़ऱॺॻॼॽॾअआइईउऊऋऌऍऎएऐऑऒओऔॠॡॲऄॵॶॳॴॷॸॹ०१२३४५६७८९़ंँः॒॑ािीुूृॄॅॆेैॉॊोौॢॣॏॎ्।॥॰ऽꣲ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~₹ - * - gujarati - - 132 - - 0123456789કખગઘઙચછજઝઞટઠડઢણતથદધનપફબભમયરલળવશષસહઅઆઇઈઉઊઋઌઍએઐઑઓઔ૦૧૨૩૪૫૬૭૮૯ઁંઃ઼ાિીુૂૃૄૅેૈૉોૌૢૣૺૻૼ૽૾૿્ઽ॥!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ૐ૰૱₹ - * - bengali - - 127 - - 0123456789কখগঘঙচছজঝঞটঠডঢণতথদধনপফবভমযরলশষসহড়ঢ়য়ৰৱৼঅআইঈউঊঋঌএঐওঔৠৡ০১২৩৪৫৬৭৮৯ািীুূৃেৈোৌৗ্ঽৎ৽৺৻!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ঁংঃ়৳₹ - * - tamil - - 113 - - 0123456789ஜஶஷஸஹகஙசஞடணதநபமயரலவழளறனஅஆஇஈஉஊஎஏஐஒஓஔ௦௧௨௩௪௫௬௭௮௯ாிீுூெேைொோௌ்௰௱௲!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~௳௴௵௶௷௸௹௺ஃௐ₹ - * - telugu - - 129 - - 0123456789కఖగఘఙచఛజఝఞటఠడఢణతథదధనపఫబభమయరఱలళవశషసహఴఅఆఇఈఉఊఋఌఎఏఐఒఓఔౠౡ౦౧౨౩౪౫౬౭౮౯౸౹౺౻ాిీుూృౄెేైొోౌౢౣ్ఽ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ఁంః₹ - * - kannada - - 124 - - 0123456789ಕಖಗಘಙಚಛಜಝಞಟಠಡಢಣತಥದಧನಪಫಬಭಮಯರಲವಶಷಸಹಳಅಆಇಈಉಊಋೠಌೡಎಏಐಒಓಔ೦೧೨೩೪೫೬೭೮೯ಾಿೀುೂೃೄೆೇೈೊೋೌ್।॥ೱೲ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ಂಃಁ₹ - * - sinhala - - 129 - - 0123456789කඛගඝඞඟචඡජඣඤඥඦටඨඩඪණඬතථදධනඳපඵබභමඹයරලවශෂසහළෆඅආඇඈඉඊඋඌඍඎඏඐඑඒඓඔඕඖ෦෧෨෩෪෫෬෭෮෯ාැෑිීුූෙේෛොෝෞ්෴!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ංඃ₹ - * - malayalam - - 132 - - 0123456789ൺൻർൽൾൿകഖഗഘങചഛജഝഞടഠഡഢണതഥദധനപഫബഭമയരറലളഴവശഷസഹഅആഇഈഉഊഋൠഌൡഎഏഐഒഓഔ൦൧൨൩൪൫൬൭൮൯ാിീുൂൃൄൢൣെേൈൊോൌ്!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ഃ൹ഽ൏ം₹ - * - punjabi - - 122 - - 0123456789ਕਖਗਘਙਚਛਜਝਞਟਠਡਢਣਤਥਦਧਨਪਫਬਭਮਯਰਲਵਸ਼ਸਹਖ਼ਗ਼ਜ਼ਫ਼ੜਲ਼ਅਆਇਈਉਊਏਐਓਔੲੳ੦੧੨੩੪੫੬੭੮੯ਂ਼ਾਿੀੁੂੇੈੋੌੑੰੱੵ੍।॥!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ੴ₹ - * - odia - - 131 - - 0123456789କଖଗଘଙଚଛଜଝଞଟଠଡଢଣତଥଦଧନପଫବଭମଯରଲଳଵଶଷସହୟୱଡ଼ଢ଼ଅଆଇଈଉଊଋଌଏଐଓଔୡୠ୦୧୨୩୪୫୬୭୮୯୲୳୴୵୶୷ାିୀୁୂୃୄେୈୋୌୢୣ୍ଽ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ଂଃଁ଼୰₹ - * - khmer - - 144 - - 0123456789កខគឃងចឆជឈញដឋឌឍណតថទធនបផពភមយរលវឝឞសហឡអឣឤឥឦឧឨឩឪឫឬឭឮឯឰឱឲឳ០១២៣៤៥៦៧៨៩ាិីឹឺុូួើឿៀេែៃោៅ្ំះៈ៉៊់៌៍៎៏័៑៓៝។៕៖៘៙៚ៗៜ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~៛ - * - armenian - - 131 - - ԱԲԳԴԵԶԷԸԹԺԻԼԽԾԿՀՁՂՃՄՅՆՇՈՉՊՋՌՍՎՏՐՑՒՓՔՕՖՙՠաբգդեզէըթժիլխծկհձղճմյնշոչպջռսվտրցւփքօֆևֈ0123456789!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~՚՛՜՝՞՟։֊֏ - * - sudanese - - 106 - - 0123456789᮰᮱᮲᮳᮴᮵᮶᮷᮸᮹ᮊᮋᮌᮍᮎᮏᮐᮑᮒᮓᮔᮕᮖᮗᮘᮙᮚᮛᮜᮝᮞᮟᮠᮮᮯᮺᮻᮼᮽᮾᮿᮃᮄᮅᮆᮇᮈᮉᮀᮁᮂᮡᮢᮣᮤᮥᮦᮧᮨᮩ᮪᮫ᮬᮭ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ - * - thai - - 129 - - 0123456789๐๑๒๓๔๕๖๗๘๙!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~๏๚๛ๆฯกขฃคฅฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลฦวศษสหฬอฮะาำเแโใไๅัิีึืฺุู็่้๊๋์ํ๎฿ - * - lao - - 124 - - 0123456789໐໑໒໓໔໕໖໗໘໙!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ໆໞໟຯກຂຄຆງຈຉຊຌຍຎຏຐຑຒຓດຕຖທຘນບປຜຝພຟຠມຢຣລວຨຩສຫຬອຮະາຳຽເແໂໃໄໜໝັິີຶື຺ຸູົຼ່້໊໋໌ໍ - * - burmese - - 162 - - 0123456789၀၁၂၃၄၅၆၇၈၉႐႑႒႓႔႕႖႗႘႙ကခဂဃငစဆဇဈဉညဋဌဍဎဏတထဒဓနပဖဗဘမယရလဝသဟဠအၐၑၒၓၔၕၚၛၜၝၡၥၦၮၯၰၵၶၷၸၹၺၻၼၽၾၿႀႁႎဣဤဥဦဧဩဪဿ့းံါာိီုူေဲဳဴဵျြွှ္်၊။၌၍၎၏ၤၗ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ - * - javanese - - 124 - - 0123456789꧐꧑꧒꧓꧔꧕꧖꧗꧘꧙ꦏꦐꦑꦒꦓꦔꦕꦖꦗꦘꦙꦚꦛꦜꦝꦞꦟꦠꦡꦢꦣꦤꦥꦦꦧꦨꦩꦪꦫꦬꦭꦮꦯꦰꦱꦲꦄꦅꦆꦇꦈꦉꦊꦋꦌꦍꦎꦴꦵꦶꦷꦸꦹꦺꦻꦼꦀꦁꦂꦃ꦳ꦽꦾꦿ꧀꧈꧉꧊꧋꧌꧍ꧏ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~ - * - georgian - - 177 - - 0123456789ႠႡႢႣႤႥႦႧႨႩႪႫႬႭႮႯႰႱႲႳႴႵႶႷႸႹႺႻႼႽႾႿჀჁჂჃჄჅჇჍაბგდევზთიკლმნოპჟრსტუფქღყშჩცძწჭხჯჰჱჲჳჴჵჶჷჸჹჺჼჽჾჿᲐᲑᲒᲓᲔᲕᲖᲗᲘᲙᲚᲛᲜᲝᲞᲟᲠᲡᲢᲣᲤᲥᲦᲧᲨᲩᲪᲫᲬᲭᲮᲯᲰᲱᲲᲳᲴᲵᲶᲷᲸᲹᲺᲽᲾᲿ!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~჻₾ - * - ethiopic - - 418 - - ሀሁሂሃሄህሆሇለሉሊላሌልሎሏሐሑሒሓሔሕሖሗመሙሚማሜምሞሟሠሡሢሣሤሥሦሧረሩሪራሬርሮሯሰሱሲሳሴስሶሷሸሹሺሻሼሽሾሿቀቁቂቃቄቅቆቇቈቊቋቌቍቐቑቒቓቔቕቖቘቚቛቜቝበቡቢባቤብቦቧቨቩቪቫቬቭቮቯተቱቲታቴትቶቷቸቹቺቻቼችቾቿኀኁኂኃኄኅኆኇኈኊኋኌኍነኑኒናኔንኖኗኘኙኚኛኜኝኞኟአኡኢኣኤእኦኧከኩኪካኬክኮኯኰኲኳኴኵኸኹኺኻኼኽኾዀዂዃዄዅወዉዊዋዌውዎዏዐዑዒዓዔዕዖዘዙዚዛዜዝዞዟዠዡዢዣዤዥዦዧየዩዪያዬይዮዯደዱዲዳዴድዶዷዸዹዺዻዼዽዾዿጀጁጂጃጄጅጆጇገጉጊጋጌግጎጏጐጒጓጔጕጘጙጚጛጜጝጞጟጠጡጢጣጤጥጦጧጨጩጪጫጬጭጮጯጰጱጲጳጴጵጶጷጸጹጺጻጼጽጾጿፀፁፂፃፄፅፆፇፈፉፊፋፌፍፎፏፐፑፒፓፔፕፖፗፘፙፚᎀᎁᎂᎃᎄᎅᎆᎇᎈᎉᎊᎋᎌᎍᎎᎏ፩፪፫፬፭፮፯፰፱፲፳፴፵፶፷፸፹፺፻፼0123456789፠፡።፣፤፥፦፧፨!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~£€¥¢฿ - * - japanese - - 2383 - - 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- * - korean - - 11237 - - 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- * - simplified_chinese - - 6839 - - 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-.. _document_structure: - -Document structure ------------------- - -Structural organization of the documents. - -Word -^^^^ -A Word is an uninterrupted sequence of characters. - -.. autoclass:: Word - -Prediction -^^^^^^^^^^ -A Prediction is a Word with an additional crop orientation field indicating the detected text rotation angle. - -.. autoclass:: Prediction - -Line -^^^^ -A Line is a collection of Words aligned spatially and meant to be read together (on a two-column page, on the same horizontal, we will consider that there are two Lines). - -.. autoclass:: Line - -Artefact -^^^^^^^^ - -An Artefact is a non-textual element (e.g. QR code, picture, chart, signature, logo, etc.). - -.. autoclass:: Artefact - -LayoutElement -^^^^^^^^^^^^^ - -A LayoutElement is a region predicted by a layout detection model (e.g. Title, Text, Table, Page-header, Page-footer). Layout regions are attached to a :class:`Page` when the ``ocr_predictor`` / ``kie_predictor`` is run with ``detect_layout=True``. - -.. autoclass:: LayoutElement - -Block -^^^^^ -A Block is a collection of Lines (e.g. an address written on several lines) and Artefacts (e.g. a graph with its title underneath). - -.. autoclass:: Block - -Page -^^^^ - -A Page is a collection of Blocks that were on the same physical page. - -.. autoclass:: Page - - .. automethod:: show - .. automethod:: items_in_reading_order - .. automethod:: export_as_markdown - .. automethod:: export_as_asciidoc - .. automethod:: export_as_html - .. automethod:: export_as_xml - .. automethod:: export - .. automethod:: render - .. automethod:: export_as - - -KIEPage -^^^^^^^ - -A KIEPage is returned by the :py:meth:`kie_predictor `. It groups predictions by -semantic class rather than by spatial layout. - -.. autoclass:: KIEPage - - .. automethod:: show - .. automethod:: export_as_markdown - .. automethod:: export_as_asciidoc - .. automethod:: export_as_html - .. automethod:: export_as_xml - .. automethod:: export - .. automethod:: render - .. automethod:: export_as - - -Document -^^^^^^^^ - -A Document is a collection of Pages. - -.. autoclass:: Document - - .. automethod:: show - .. automethod:: export_as_markdown - .. automethod:: export_as_asciidoc - .. automethod:: export_as_xml - .. automethod:: export_as_html - .. automethod:: export - .. automethod:: render - .. automethod:: export_as - - -KIEDocument -^^^^^^^^^^^ - -A KIEDocument is a collection of :class:`KIEPage` elements, returned by the -:py:meth:`kie_predictor `. - -.. autoclass:: KIEDocument - - .. automethod:: show - - -File reading ------------- - -High-performance file reading and conversion to processable structured data. - -.. autofunction:: read_pdf - -.. autofunction:: read_img_as_numpy - -.. autofunction:: read_img_as_tensor - -.. autofunction:: decode_img_as_tensor - -.. autofunction:: read_html - - -.. autoclass:: DocumentFile - - .. automethod:: from_pdf - - .. automethod:: from_url - - .. automethod:: from_images - - -.. _reading_order: - -Reading order -------------- - -The reading-order-aware export of a :class:`Document` / :class:`Page` to Markdown, AsciiDoc, HTML, or XML is available -through the ``export_as_markdown`` / ``export_as_asciidoc`` / ``export_as_html`` / ``export_as_xml`` / ``export_as`` / ``export`` / ``render`` methods documented above, which -delegate to the exporters of :mod:`doctr.io.exporters`. -The underlying ordering primitives live in :mod:`doctr.models.reading_order`. - -Every export path shares the same linearization, so ``render()``, ``export()``, ``export_as_xml()`` and the -Markdown / AsciiDoc / HTML exports all present the content in the same order. The result is memoized on the -page, so exporting one page to several formats orders it only once. - -.. currentmodule:: doctr.io - -.. autoclass:: TextExporter - :members: export_page, export_kie_page, export_document - -.. autoclass:: MarkdownExporter - :members: export_page, export_kie_page, export_document - -.. autoclass:: AsciiDocExporter - :members: export_page, export_kie_page, export_document - -.. autoclass:: HTMLExporter - :members: export_page, export_kie_page, export_document - -.. autoclass:: XMLExporter - :members: export_page, export_kie_page, export_document - -.. autofunction:: doctr.io.exporters.page_reading_order - -.. autofunction:: doctr.io.exporters.predictions_in_reading_order - -.. autofunction:: doctr.io.exporters.to_json_safe - -Figures -------- - -When the predictor runs the layout model (``detect_layout=True`` or ``detect_tables=True``), the detected figures -take part in the reading order, and the Markdown, AsciiDoc and HTML exports render them according to their -``images`` argument, an image mode or a configured :class:`FigureEncoder`: - -* ``'placeholder'`` (default): a comment marks each figure (```` in Markdown and HTML, ``// image`` in - AsciiDoc) -* ``'none'``: the figures are left out -* ``'embedded'``: each figure is cropped and inlined as a base64 data URI -* ``'referenced'``: each crop is written to an image directory and linked by its relative path, which needs a - configured encoder: ``images=FigureEncoder("referenced", image_dir=...)``. Set ``path_prefix`` when the export is - not written to the working directory: it locates the image directory from the export. The file names carry the - position and a hash of the figure (e.g. ``page1_figure2-3fa2b1c9.png``), so several documents can share a - directory. - -.. note:: - Once a figure carries its pixels (``'embedded'`` and ``'referenced'``), the text recognized inside it is left - out of the export, since the image shows it, and its caption becomes the alternative text of the image. A figure - which cannot be cropped (e.g. on a page restored from a JSON export) falls back to a placeholder and keeps its - text. ``page.items_in_reading_order(include_figures=True)`` returns the figures along with the blocks and tables. - -.. code:: python - - from doctr.io import DocumentFile, FigureEncoder - from doctr.models import ocr_predictor - - predictor = ocr_predictor(pretrained=True, detect_layout=True) - doc = predictor(DocumentFile.from_pdf("report.pdf")) - - # A self-contained Markdown file - markdown = doc.export_as_markdown(images="embedded") - # ... or one that links to the crops written to `assets` - markdown = doc.export_as_markdown(images=FigureEncoder("referenced", image_dir="assets")) - -The caption also stays visible: an italic paragraph under the image in Markdown, a ``
        `` in HTML and a -block title in AsciiDoc. Plain text never carries figures, and hOCR positions them as ``ocr_photo`` areas. A figure -lying (at least half) in a page header or footer, e.g. a logo, is read with that furniture, and left out with it by -``include_furniture=False``. - -The command line exposes the same options: ``--images`` picks the mode, ``--image_format`` the file format, and in -``referenced`` mode the crops are written to ``--image_dir`` (by default ``_images`` next to the output): - -.. code:: bash - - doctr-cli --input_path report.pdf --detect_layout --output report.md --images referenced - -.. autoclass:: FigureEncoder - :members: resolve, source, enabled, materializes - -.. autofunction:: crop_layout_region - -.. autofunction:: encode_crop - -.. autofunction:: picture_regions - -.. autofunction:: is_picture_region - -.. autofunction:: is_picture_label diff --git a/docs/source/modules/transforms.rst b/docs/source/modules/transforms.rst deleted file mode 100644 index 0a1a74a5d1..0000000000 --- a/docs/source/modules/transforms.rst +++ /dev/null @@ -1,39 +0,0 @@ -doctr.transforms -================ - -.. currentmodule:: doctr.transforms - -Data transformations are part of both training and inference procedure. Drawing inspiration from the design of `torchvision `_, we express transformations as composable modules. - - -Supported transformations -------------------------- -Here are all transformations that are available through docTR: - -.. currentmodule:: doctr.transforms.modules - -.. autoclass:: Resize -.. autoclass:: GaussianNoise -.. autoclass:: ChannelShuffle -.. autoclass:: RandomHorizontalFlip -.. autoclass:: RandomShadow - -.. autoclass:: RandomGlare - -.. autoclass:: RandomLighting - -.. autoclass:: RandomPerspective -.. autoclass:: RandomResize - - -Composing transformations ---------------------------------------------- -It is common to require several transformations to be performed consecutively. - -.. autoclass:: SampleCompose -.. autoclass:: ImageTransform -.. autoclass:: ColorInversion -.. autoclass:: OneOf -.. autoclass:: RandomApply -.. autoclass:: RandomRotate -.. autoclass:: RandomCrop diff --git a/docs/source/modules/utils.rst b/docs/source/modules/utils.rst deleted file mode 100644 index a6f4be3f13..0000000000 --- a/docs/source/modules/utils.rst +++ /dev/null @@ -1,67 +0,0 @@ -doctr.utils -=========== - -This module regroups non-core features that are complementary to the rest of the package. - -.. currentmodule:: doctr.utils - - -Visualization -------------- -Easy-to-use functions to make sense of your model's predictions. - -.. currentmodule:: doctr.utils.visualization - -.. autofunction:: visualize_page - -.. autofunction:: visualize_kie_page - -.. autofunction:: draw_boxes - -Reconstitution ---------------- - -.. currentmodule:: doctr.utils.reconstitution - -.. autofunction:: synthesize_page - -.. autofunction:: synthesize_kie_page - - -.. _metrics: - -Task evaluation ---------------- -Implementations of task-specific metrics to easily assess your model performances. - -.. currentmodule:: doctr.utils.metrics - -.. autoclass:: TextMatch - - .. automethod:: update - .. automethod:: summary - -.. autoclass:: LocalizationConfusion - - .. automethod:: update - .. automethod:: summary - -.. autoclass:: OCRMetric - - .. automethod:: update - .. automethod:: summary - -.. autoclass:: DetectionMetric - - .. automethod:: update - .. automethod:: summary - -.. autoclass:: ObjectDetectionMetric - - .. automethod:: update - .. automethod:: summary - -.. autoclass:: TableCellMetric - - .. automethod:: update - .. automethod:: summary diff --git a/docs/source/notebooks.rst b/docs/source/notebooks.rst deleted file mode 120000 index 6c3d7bfec2..0000000000 --- a/docs/source/notebooks.rst +++ /dev/null @@ -1 +0,0 @@ -../../notebooks/README.rst \ No newline at end of file diff --git a/docs/source/training/custom_models.md b/docs/source/training/custom_models.md new file mode 100644 index 0000000000..392b34546f --- /dev/null +++ b/docs/source/training/custom_models.md @@ -0,0 +1,175 @@ +# Using your trained models + +Every predictor factory accepts a model **instance** instead of an architecture name. To use your checkpoint, +build the architecture with the same settings as for training, load the weights with `from_pretrained`, and pass +the model to the predictor. Pretrained and custom models can be mixed freely. + +:::{tip} +`from_pretrained` accepts a local path or a URL. The `.json` file written next to the checkpoint +records the settings to rebuild the model with (vocabulary, class names...). +::: + +## Detection and recognition models + +::::{tab-set} + +:::{tab-item} Custom detection +```python +from doctr.models import db_resnet50, ocr_predictor + +det_model = db_resnet50(pretrained=False, pretrained_backbone=False) +det_model.from_pretrained("path/to/detection.pt", map_location="cpu") + +predictor = ocr_predictor(det_arch=det_model, reco_arch="crnn_vgg16_bn", pretrained=True) +``` +::: + +:::{tab-item} Custom recognition +```python +from doctr.models import crnn_vgg16_bn, ocr_predictor + +reco_model = crnn_vgg16_bn(pretrained=False, pretrained_backbone=False) +reco_model.from_pretrained("path/to/recognition.pt", map_location="cpu") + +predictor = ocr_predictor(det_arch="fast_base", reco_arch=reco_model, pretrained=True) +``` +::: + +:::{tab-item} Both +```python +from doctr.models import crnn_vgg16_bn, db_resnet50, ocr_predictor + +det_model = db_resnet50(pretrained=False, pretrained_backbone=False) +det_model.from_pretrained("path/to/detection.pt") +reco_model = crnn_vgg16_bn(pretrained=False, pretrained_backbone=False) +reco_model.from_pretrained("path/to/recognition.pt") + +# pretrained=True still loads the pretrained orientation classifiers when they are needed +predictor = ocr_predictor(det_arch=det_model, reco_arch=reco_model, pretrained=True) +``` +::: +:::: + +### Another vocabulary + +A recognition model trained on another vocabulary than the default (`french`) must be built with that vocabulary, +so that its output layer matches the checkpoint: + +```python +from doctr.datasets import VOCABS +from doctr.models import crnn_vgg16_bn, ocr_predictor + +reco_model = crnn_vgg16_bn(pretrained=False, pretrained_backbone=False, vocab=VOCABS["german"]) +reco_model.from_pretrained("path/to/recognition.pt") + +predictor = ocr_predictor(reco_arch=reco_model, pretrained=True) +``` + +### KIE classes + +A multi-class detection model is built with its class names, and used with the +[KIE predictor](../user_guide/kie.md): + +```python +from doctr.models import db_resnet50, kie_predictor + +det_model = db_resnet50(pretrained=False, pretrained_backbone=False, class_names=["total", "date"]) +det_model.from_pretrained("path/to/detection.pt") + +predictor = kie_predictor(det_arch=det_model, reco_arch="crnn_vgg16_bn", pretrained=True) +``` + +## Layout models + +A layout model trained on your own classes is passed through `layout_arch`. The detected regions carry your class +names: + +```python +from doctr.models import lw_detr_s, ocr_predictor + +layout_model = lw_detr_s(pretrained=False, class_names=["heading", "paragraph", "figure", "table"]) +layout_model.from_pretrained("path/to/layout.pt") + +predictor = ocr_predictor(pretrained=True, detect_layout=True, layout_arch=layout_model) +result = predictor(pages) +print([(region.type, region.confidence) for region in result.pages[0].layout]) +``` + +:::{note} +Table recognition (`detect_tables=True`) crops the regions labelled `Table`: keep this class name if your layout +model should feed the table model. +::: + +## Table structure models + +```python +from doctr.models import ocr_predictor, tablecenternet +from doctr.models.table_structure.zoo import table_predictor + +table_model = tablecenternet(pretrained=False) +table_model.from_pretrained("path/to/table.pt") + +predictor = ocr_predictor(pretrained=True, detect_tables=True) +predictor.table_predictor = table_predictor(table_model) # replace the default table model +``` + +## Orientation classifiers + +The orientation classifiers are used with `assume_straight_pages=False`, `straighten_pages=True` or +`detect_orientation=True`. Only `mobilenet_v3_small` based models are supported. Replace the default ones after +building the predictor: + +```python +from doctr.models import ( + crop_orientation_predictor, + mobilenet_v3_small_crop_orientation, + mobilenet_v3_small_page_orientation, + ocr_predictor, + page_orientation_predictor, +) + +page_model = mobilenet_v3_small_page_orientation(pretrained=False) +page_model.from_pretrained("path/to/page_orientation.pt") +crop_model = mobilenet_v3_small_crop_orientation(pretrained=False) +crop_model.from_pretrained("path/to/crop_orientation.pt") + +predictor = ocr_predictor(pretrained=True, assume_straight_pages=False, detect_orientation=True) +predictor.page_orientation_predictor = page_orientation_predictor(page_model) +predictor.crop_orientation_predictor = crop_orientation_predictor(crop_model) +``` + +## Custom pre-processing + +To change how images are resized and normalized (e.g. a model trained with another input size or other +statistics), assemble the predictors yourself: + +```python +from doctr.models import crnn_vgg16_bn, db_resnet50 +from doctr.models.detection.predictor import DetectionPredictor +from doctr.models.predictor import OCRPredictor +from doctr.models.preprocessor import PreProcessor +from doctr.models.recognition.predictor import RecognitionPredictor + +det_model = db_resnet50(pretrained=False, pretrained_backbone=False) +det_model.from_pretrained("path/to/detection.pt") +reco_model = crnn_vgg16_bn(pretrained=False, pretrained_backbone=False) +reco_model.from_pretrained("path/to/recognition.pt") + +det_predictor = DetectionPredictor( + PreProcessor((1024, 1024), batch_size=1, mean=(0.798, 0.785, 0.772), std=(0.264, 0.2749, 0.287)), + det_model, +) +reco_predictor = RecognitionPredictor( + PreProcessor( + (32, 128), preserve_aspect_ratio=True, batch_size=32, mean=(0.694, 0.695, 0.693), std=(0.299, 0.296, 0.301) + ), + reco_model, +) + +predictor = OCRPredictor(det_predictor, reco_predictor) +``` + +## Models from the Hugging Face Hub + +Models shared on the Hub are loaded with {func}`~doctr.models.factory.from_hub`, which restores the architecture and its +settings automatically. See [](sharing_models.md). diff --git a/docs/source/training/datasets.md b/docs/source/training/datasets.md new file mode 100644 index 0000000000..ead07e4a2a --- /dev/null +++ b/docs/source/training/datasets.md @@ -0,0 +1,137 @@ +# Datasets + +{mod}`doctr.datasets` provides ready-to-use public datasets, synthetic data generators, and loaders for your own +annotations. They all return samples in the format expected by the docTR models and training scripts. + +## Built-in datasets + +Public datasets are downloaded and extracted on first use (`download=True`) into `~/.cache/doctr/datasets`. + +```python +from doctr.datasets import CORD + +train_set = CORD(train=True, download=True) +sample = train_set[0] +image, target = sample.image, sample.target +``` + +The same dataset can be loaded for each task: + +::::{tab-set} + +:::{tab-item} OCR (default) +Full pages, with the boxes **and** the transcriptions of the words: + +```python +train_set = CORD(train=True, download=True) +target = train_set[0].target # {"boxes": array of shape (N, 4), "labels": [str, ...]} +``` +::: + +:::{tab-item} Detection +Full pages, with the boxes of the words only: + +```python +train_set = CORD(train=True, download=True, detection_task=True) +``` +::: + +:::{tab-item} Recognition +Word crops, with their transcription: + +```python +train_set = CORD(train=True, download=True, recognition_task=True) +image, word = train_set[0].image, train_set[0].target +``` +::: +:::: + +Add `use_polygons=True` to get rotated boxes as `(4, 2)` polygons instead of straight boxes (for recognition, +crops are then extracted along the rotated box). + +### Available datasets + +| Dataset | Detection (train / test pages) | Recognition (train / test crops) | Notes | +| --- | ---: | ---: | --- | +| {class}`~doctr.datasets.FUNSD` | 149 / 50 | 21,888 / 8,707 | English forms | +| {class}`~doctr.datasets.SROIE` | 626 / 360 | 33,608 / 19,342 | Receipts, upper-case labels | +| {class}`~doctr.datasets.CORD` | 800 / 100 | 19,370 / 2,186 | Receipts | +| {class}`~doctr.datasets.IIIT5K` | 2,000 / 3,000 | 2,000 / 3,000 | Scene text | +| {class}`~doctr.datasets.SVT` | 100 / 249 | 257 / 647 | Street view, upper-case labels | +| {class}`~doctr.datasets.SVHN` | 33,402 / 13,068 | 73,257 / 26,032 | House numbers, digits only | +| {class}`~doctr.datasets.SynthText` | 772,875 / 85,875 | ~7,100,000 / 707,470 | Synthetic | +| {class}`~doctr.datasets.IC03` | 246 / 249 | 1,156 / 1,107 | Scene text | +| {class}`~doctr.datasets.IC13` | 229 / 233 | 849 / 1,095 | Scene text, manual download | +| {class}`~doctr.datasets.IMGUR5K` | 7,149 / 796 | 207,901 / 22,672 | Handwritten, manual download | +| {class}`~doctr.datasets.MJSynth` | — | 7,581,382 / 1,337,891 | Synthetic words, manual download | +| {class}`~doctr.datasets.IIITHWS` | — | 7,141,797 / 793,533 | Handwritten words, manual download | +| {class}`~doctr.datasets.WILDRECEIPT` | 1,268 / 472 | 49,377 / 19,598 | Receipts, manual download | +| {class}`~doctr.datasets.COCOTEXT` | 13,880 / 3,261 | 59,130 / 13,258 | Natural images, legible text only, manual download | +| {class}`~doctr.datasets.DocArtefacts` | 2,700 / 300 | — | Object detection: QR codes, bar codes, logos, photos | + +Datasets marked *manual download* must be fetched from their authors' website first: pass the paths of the +extracted files to the constructor (see the [API reference](../api/datasets.md)). + +## Synthetic data + +Generators render random characters or words on the fly, which is useful to pre-train a model or to add an +alphabet to it. Both are used by the reference training scripts when no dataset is given. + +```python +from doctr.datasets import VOCABS, CharacterGenerator, WordGenerator + +# Single characters, e.g. to train a character classifier +chars = CharacterGenerator(vocab=VOCABS["german"], num_samples=1000) + +# Random words of 1 to 12 characters +words = WordGenerator(vocab=VOCABS["german"], min_chars=1, max_chars=12, num_samples=10000) +sample = words[0] # sample.image: rendered word, sample.target: its text +``` + +Both accept a `font_family` (one font or a list of fonts) to render with your own fonts. + +## Your own data + +Load annotated data stored in the [training format](train.md) of each task: + +| Class | Task | Expected files | +| --- | --- | --- | +| {class}`~doctr.datasets.DetectionDataset` | Text detection | `images/` + `labels.json` with polygons (optionally per class) | +| {class}`~doctr.datasets.RecognitionDataset` | Text recognition | word images + `labels.json` mapping each image to its text | +| {class}`~doctr.datasets.OCRDataset` | End-to-end OCR | `images/` + `labels.json` with boxes and transcriptions | +| {class}`~doctr.datasets.LayoutDataset` | Layout analysis | `images/` + `labels.json` with polygons and classes | +| {class}`~doctr.datasets.TableStructureDataset` | Table structure | `images/` + `labels.json` with cells and their logical coordinates | +| {class}`~doctr.datasets.OrientationDataset` | Orientation classification | a folder of upright images | + +```python +from doctr.datasets import DetectionDataset, OCRDataset, RecognitionDataset + +det_set = DetectionDataset(img_folder="path/to/images", label_path="path/to/labels.json") +reco_set = RecognitionDataset(img_folder="path/to/images", labels_path="path/to/labels.json") +ocr_set = OCRDataset(img_folder="path/to/images", label_file="path/to/labels.json") +``` + +## Transforms and batching + +Pass transformations with `img_transforms` (applied to the image) and `sample_transforms` (applied to the image +and its target, e.g. rotations). Datasets return images of different sizes, so resize them before batching. Batch +with a PyTorch {class}`~torch.utils.data.DataLoader` and the `collate_fn` of the dataset: + +```python +from torch.utils.data import DataLoader + +from doctr import transforms as T +from doctr.datasets import RecognitionDataset + +train_set = RecognitionDataset( + img_folder="path/to/images", + labels_path="path/to/labels.json", + img_transforms=T.Resize((32, 128), preserve_aspect_ratio=True), +) + +train_loader = DataLoader(train_set, batch_size=32, shuffle=True, collate_fn=train_set.collate_fn) +images, targets = next(iter(train_loader)) # images: (32, 3, 32, 128) tensor, targets: list of 32 strings +``` + +{mod}`doctr.transforms` provides document-specific augmentations (shadows, glare, perspective, rotations...). See +the [API reference](../api/transforms.md) and the reference training scripts for complete pipelines. diff --git a/docs/source/training/index.md b/docs/source/training/index.md new file mode 100644 index 0000000000..7cd3fb8b10 --- /dev/null +++ b/docs/source/training/index.md @@ -0,0 +1,63 @@ +# Training + +The pretrained models work well on common printed documents. When your documents are different (another +language, handwriting, unusual fonts, specific layouts, fields to extract), **fine-tuning** a model on a few hundred +annotated samples usually brings a large improvement. + +## The workflow + +```{raw} html +
          +
        1. Collect and annotate a representative sample of your documents, e.g. with the + docTR Labeler.
          2. +
        2. Format the data as an images/ folder and a labels.json file, + following the format of each task.
          3. +
        3. Train with the reference script of the task, starting from the pretrained weights.
          4. +
        4. Evaluate the checkpoint on held-out data with the evaluation script.
          5. +
        5. Load your checkpoint into a predictor, alongside the pretrained models.
          6. +
        6. Share it on the Hugging Face Hub, if you like.
          7. +
        +``` + +## In this section + +::::{grid} 1 2 2 2 +:gutter: 3 + +:::{grid-item-card} {octicon}`database;1.1em` Datasets +:link: datasets +:link-type: doc + +Built-in public datasets, synthetic data generators and loaders for your own data. +::: + +:::{grid-item-card} {octicon}`mortar-board;1.1em` Training a model +:link: train +:link-type: doc + +Data formats, training scripts, multi-GPU and mixed precision, for every task. +::: + +:::{grid-item-card} {octicon}`plug;1.1em` Using your trained models +:link: custom_models +:link-type: doc + +Load your checkpoints into the OCR, KIE and task-specific predictors. +::: + +:::{grid-item-card} {octicon}`share;1.1em` Sharing models +:link: sharing_models +:link-type: doc + +Publish and load models with the Hugging Face Hub, and browse community models. +::: +:::: + +```{toctree} +:hidden: + +datasets +train +custom_models +sharing_models +``` diff --git a/docs/source/training/sharing_models.md b/docs/source/training/sharing_models.md new file mode 100644 index 0000000000..62450d06fe --- /dev/null +++ b/docs/source/training/sharing_models.md @@ -0,0 +1,70 @@ +# Sharing models + +docTR is integrated with the [Hugging Face Hub](https://huggingface.co/models?library=doctr): load models shared by +the community in one line, and publish your own. + +## Load a model from the Hub + +{func}`~doctr.models.factory.from_hub` downloads a model and rebuilds it with its configuration (architecture, vocabulary, +classes...). Plug it into any predictor: + +```python +from doctr.io import DocumentFile +from doctr.models import from_hub, ocr_predictor + +det_model = from_hub("Felix92/doctr-torch-db-mobilenet-v3-large") +reco_model = from_hub("Felix92/doctr-torch-parseq-multilingual-v1") + +predictor = ocr_predictor(det_arch=det_model, reco_arch=reco_model) +result = predictor(DocumentFile.from_images("path/to/page.jpg")) +``` + +## Publish a model + +You need a [Hugging Face account](https://huggingface.co/join) and [Git LFS](https://git-lfs.com) installed. + +```python +from doctr.models import login_to_hub, push_to_hf_hub, recognition + +login_to_hub() # paste an access token with write permissions + +model = recognition.crnn_mobilenet_v3_large(pretrained=True) +push_to_hf_hub( + model, + model_name="doctr-crnn-mobilenet-v3-large-french-v1", + task="recognition", # classification, detection, recognition, layout or table_structure + arch="crnn_mobilenet_v3_large", +) +``` + +Existing repositories are never overwritten. The reference training scripts can also publish the model at the end +of the training: + +```bash +python references/recognition/train.py crnn_mobilenet_v3_large --name doctr-crnn-mobilenet-v3-large --push-to-hub +``` + +### Naming conventions + +| Task | Suggested name | +| --- | --- | +| Classification | `doctr--` | +| Detection | `doctr-` | +| Recognition | `doctr--` | +| Layout | `doctr-` | +| Table structure | `doctr-` | + +## Community models + +Models trained and shared by the community. Open a pull request to add yours! + +| Task | Architecture | Repository | Language | +| --- | --- | --- | --- | +| Detection | `db_resnet50` | [`rania-sr/doctr-Detection-model-v1-arabic`](https://huggingface.co/rania-sr/doctr-Detection-model-v1-arabic) | Arabic | +| Recognition | `parseq` | [`Felix92/doctr-torch-parseq-multilingual-v1`](https://huggingface.co/Felix92/doctr-torch-parseq-multilingual-v1) | Multilingual | +| Recognition | `parseq` | [`rania-sr/doctr-model-v1-arabic`](https://huggingface.co/rania-sr/doctr-model-v1-arabic) | Arabic | +| Recognition | `crnn_vgg16_bn` | [`tilman-rassy/doctr-crnn-vgg16-bn-fascan-v1`](https://huggingface.co/tilman-rassy/doctr-crnn-vgg16-bn-fascan-v1) | French + German + § | + +Dummy models, handy for tests: [`Felix92/doctr-dummy-torch-resnet18`](https://huggingface.co/Felix92/doctr-dummy-torch-resnet18) (classification), +[`Felix92/doctr-dummy-torch-lw-detr-s`](https://huggingface.co/Felix92/doctr-dummy-torch-lw-detr-s) (layout) and +[`Felix92/doctr-dummy-torch-tablecenternet`](https://huggingface.co/Felix92/doctr-dummy-torch-tablecenternet) (table structure). diff --git a/docs/source/training/train.md b/docs/source/training/train.md new file mode 100644 index 0000000000..fa0b22657e --- /dev/null +++ b/docs/source/training/train.md @@ -0,0 +1,233 @@ +# Training a model + +The [`references/`](https://github.com/mindee/doctr/tree/main/references) folder of the repository contains +ready-to-use training, evaluation and latency scripts for every task. This page summarizes how to use them; each +task folder has a README with all the details. + +## Setup + +The scripts live in the repository, not in the package. Clone it and install the extra requirements: + +```bash +git clone https://github.com/mindee/doctr.git +cd doctr +pip install -e . +pip install -r references/requirements.txt +``` + +All scripts share the same interface: the architecture as first argument, `--train_path` and `--val_path` +pointing to your data, and options such as `--epochs`, `--batch_size`, `--lr`, `--pretrained` (start from the +pretrained weights, recommended for fine-tuning) or `--name` (experiment name). Run any script with `--help` to +list them all. + +:::{tip} +Annotating data is the hardest part. The [docTR Labeler](../community/tools.md#doctr-labeler) lets you draw and +correct polygons with automatic suggestions, and exports the formats below. +::: + +## Data formats + +Every task uses the same layout on disk, one folder per split: + +```text +train_set/ +├── images/ +│ ├── sample_img_01.png +│ ├── sample_img_02.png +│ └── ... +└── labels.json +``` + +`labels.json` maps each image file name to its annotation, which depends on the task. Coordinates are **absolute +pixel coordinates**. + +::::{tab-set} + +:::{tab-item} Detection +```{code-block} javascript +:caption: labels.json + +{ + "sample_img_01.png": { + "img_dimensions": [900, 600], + "img_hash": "", + "polygons": [[[x1, y1], [x2, y2], [x3, y3], [x4, y4]], ...] + } +} +``` + +For a multi-class model (e.g. for [KIE](../user_guide/kie.md)), `polygons` is a dictionary with one list per class: + +```javascript +"polygons": { + "date": [[[x1, y1], [x2, y2], [x3, y3], [x4, y4]], ...], + "total": [[[x1, y1], [x2, y2], [x3, y3], [x4, y4]], ...] +} +``` + +Every class must appear in both splits; an image without a box of a class has an empty list for it. +::: + +:::{tab-item} Recognition +`images/` contains **word crops** and `labels.json` their transcription, in UTF-8: + +```{code-block} javascript +:caption: labels.json + +{ + "img_1.jpg": "I", + "img_2.jpg": "am", + "img_3.jpg": "a", + "img_4.jpg": "Jedi" +} +``` + +Labels must not contain spaces, and their characters must be part of the vocabulary you train with. +::: + +:::{tab-item} Layout +```{code-block} javascript +:caption: labels.json + +{ + "sample_img_01.png": { + "img_dimensions": [900, 600], + "img_hash": "", + "polygons": [[[x1, y1], [x2, y2], [x3, y3], [x4, y4]], ...], + "classes": ["Title", "Text", ...] + } +} +``` +::: + +:::{tab-item} Table structure +`images/` contains images of **tables**: + +```{code-block} javascript +:caption: labels.json + +{ + "sample_img_01.png": { + "cells": [[[x1, y1], [x2, y2], [x3, y3], [x4, y4]], ...], + "logic": [[start_col, end_col, start_row, end_row], ...] + } +} +``` + +Cell polygons are ordered top-left, top-right, bottom-right, bottom-left. Logical coordinates are 0-indexed and +inclusive: a cell spanning one row and one column has equal start and end indices. +::: + +:::{tab-item} Orientation +No labels: a folder of **upright** images. The script rotates them to generate the training targets. + +```text +train_set/ +├── sample_img_01.png +├── sample_img_02.png +└── ... +``` +::: +:::: + +## Train + +### Text detection + +```bash +python references/detection/train.py db_resnet50 \ + --train_path path/to/train_set --val_path path/to/val_set \ + --epochs 5 --pretrained +``` + +Add `--rotation` to train on rotated documents. Built-in datasets can replace local folders: +`--train_datasets FUNSD CORD --val_datasets FUNSD`. +[Full guide](https://github.com/mindee/doctr/tree/main/references/detection#readme). + +### Text recognition + +```bash +python references/recognition/train.py crnn_vgg16_bn \ + --train_path path/to/train_set --val_path path/to/val_set \ + --vocab german --epochs 5 --pretrained +``` + +`--vocab` selects one of the [vocabularies](../models/vocabs.md) (`french` by default). Without any dataset, the +script trains on synthetic words generated from the vocabulary, optionally with your own fonts (`--font`). +[Full guide](https://github.com/mindee/doctr/tree/main/references/recognition#readme). + +### Layout analysis + +```bash +python references/layout/train.py lw_detr_s \ + --train_path path/to/train_set --val_path path/to/val_set --epochs 5 +``` + +[Full guide](https://github.com/mindee/doctr/tree/main/references/layout#readme). + +### Table structure + +```bash +python references/table/train.py tablecenternet \ + --train_path path/to/train_set --val_path path/to/val_set --epochs 5 +``` + +[Full guide](https://github.com/mindee/doctr/tree/main/references/table#readme), including a toy dataset to try +the pipeline end to end. + +### Orientation classifiers + +```bash +# --type page for whole pages, --type crop for word crops +python references/classification/train_orientation.py mobilenet_v3_small_page_orientation \ + --type page --train_path path/to/train_set --val_path path/to/val_set --epochs 5 +``` + +[Full guide](https://github.com/mindee/doctr/tree/main/references/classification#readme). + +## Hardware + +::::{tab-set} + +:::{tab-item} Single GPU +Every script accepts `--device`: a CUDA index (`0`), `cuda:N`, `mps` or `cpu`. By default, the script picks CUDA, +then MPS, then the CPU. + +`--amp` enables automatic mixed precision on CUDA; add `--amp-dtype bfloat16` on Ampere GPUs or newer to avoid the +overflows of float16: + +```bash +python references/detection/train.py db_resnet50 --train_path train --val_path val \ + --device 0 --amp --amp-dtype bfloat16 +``` +::: + +:::{tab-item} Multi-GPU +Launch the same scripts with [`torchrun`](https://pytorch.org/docs/stable/elastic/run.html) to train with +DistributedDataParallel: + +```bash +CUDA_VISIBLE_DEVICES=0,2 torchrun --nproc_per_node=2 references/detection/train.py db_resnet50 \ + --train_path train --val_path val --epochs 5 --backend nccl +``` +::: + +:::{tab-item} Apple Silicon +```bash +# Let the few operations MPS lacks fall back to the CPU +PYTORCH_ENABLE_MPS_FALLBACK=1 python references/detection/train.py db_resnet50 \ + --train_path train --val_path val --device mps +``` +::: +:::: + +## Outputs, logging and evaluation + +- **Checkpoints** are saved in `--output_dir` as `.pt`, next to a `.json` file + with the run metadata: architecture and task settings (vocabulary, class names...), dataset hashes, library + versions and all the arguments. +- **Tracking**: `--wb` logs to Weights & Biases and `--clearml` to ClearML. +- **Evaluation**: each task folder has an `evaluate.py` script, and a `latency.py` script to benchmark speed. +- **Sharing**: `--push-to-hub` publishes the final model on the [Hugging Face Hub](sharing_models.md). + +Then [load your checkpoint](custom_models.md) into a predictor. diff --git a/docs/source/user_guide/artefacts.md b/docs/source/user_guide/artefacts.md new file mode 100644 index 0000000000..249d1fe508 --- /dev/null +++ b/docs/source/user_guide/artefacts.md @@ -0,0 +1,56 @@ +# Artefact detection + +The `doctr.contrib` module gathers complementary features that are useful in document pipelines but are not part +of the core OCR. Its first member, {class}`~doctr.contrib.ArtefactDetector`, finds non-textual elements on a page: +**bar codes, QR codes, logos and photos**. + +:::{note} +Contrib modules run on [ONNX Runtime](https://onnxruntime.ai) instead of PyTorch. Install the extra first: + +```bash +pip install "python-doctr[contrib]" +# or, for GPU inference: pip install onnxruntime-gpu +``` +::: + +## Detect artefacts + +```python +from doctr.contrib import ArtefactDetector +from doctr.io import DocumentFile + +pages = DocumentFile.from_images(["path/to/page.jpg"]) + +detector = ArtefactDetector(batch_size=2, conf_threshold=0.5, iou_threshold=0.5) +results = detector(pages) + +for artefact in results[0]: # one list per page + print(artefact["label"], artefact["confidence"], artefact["box"]) +# e.g. qr_code 0.91 [812, 64, 1003, 255] + +detector.show() # requires the `viz` extra +``` + +Each artefact is a dict with a `label`, a `confidence` and a `box` as `[xmin, ymin, xmax, ymax]` in **pixels**. + +| Parameter | Default | Description | +| ----------------- | ------- | -------------------------------------------------------------- | +| `batch_size` | `2` | Number of pages processed at once | +| `conf_threshold` | `0.5` | Minimum confidence of a detection | +| `iou_threshold` | `0.5` | Overlap above which duplicate detections are merged (NMS) | + +## Use your own YOLOv8 model + +The detector is based on [YOLOv8](https://github.com/ultralytics/ultralytics). You can run any YOLOv8 detection +model exported to ONNX, e.g. one trained to find tables, stamps or signatures: + +```python +from doctr.contrib import ArtefactDetector + +detector = ArtefactDetector(model_path="path/to/your/model.onnx", labels=["table", "figure"]) +``` + +:::{important} +The model must be exported to ONNX **with a dynamic batch size**. Oriented bounding box (OBB) models are not +supported. +::: diff --git a/docs/source/user_guide/cli.md b/docs/source/user_guide/cli.md new file mode 100644 index 0000000000..f928c8788e --- /dev/null +++ b/docs/source/user_guide/cli.md @@ -0,0 +1,135 @@ +# Command-line interface + +`doctr-cli` runs the complete OCR pipeline on images and PDFs from your terminal, and writes the result as JSON, +plain text, Markdown, AsciiDoc, HTML or hOCR. It is installed with the package. + +```bash +doctr-cli --input_path invoice.pdf --output invoice.json +``` + +## Common recipes + +::::{tab-set} + +:::{tab-item} Text +```bash +doctr-cli --input_path scan.jpg --output scan.txt +``` +::: + +:::{tab-item} Markdown with tables +```bash +doctr-cli --input_path report.pdf --detect_tables --output report.md +``` +::: + +:::{tab-item} Several files +Every page of every input is appended to a single document: + +```bash +doctr-cli --input_path page1.jpg page2.jpg annex.pdf --output dossier.json +``` +::: + +:::{tab-item} Rotated scans +```bash +doctr-cli --input_path skewed.pdf --straighten_pages --output skewed.txt +``` +::: + +:::{tab-item} GPU & hOCR +On a CUDA device of compute capability 8.0 or newer, the models run in bfloat16: + +```bash +doctr-cli --input_path doc.pdf --device cuda:0 --output doc.xml +``` +::: + +:::{tab-item} Web page +Requires the `html` extra: + +```bash +doctr-cli --input_path https://www.example.com --output page.md +``` +::: +:::: + +The format is **inferred from the extension** of `--output` (`.json`, `.txt`, `.md`, `.adoc`, `.html`, `.xml`) and +falls back to JSON. Use `--format` to set it explicitly. + +## Arguments + +Run `doctr-cli --help` for the full list with default values. + +### Input + +| Argument | Default | Description | +| ----------------- | ------- | ------------------------------------------------------------------------------------------- | +| `--input_path` | — | **Required.** One or more images, PDF files, or `http(s)://` URLs of web pages | +| `--pdf_password` | `None` | Password of encrypted PDF files | +| `--pdf_scale` | `2` | PDF rendering scale (1 corresponds to 72 dpi) | + +### Models + +| Argument | Default | Description | +| --------------- | ----------------- | --------------------------------------------------------------------------------- | +| `--det_arch` | `fast_base` | Detection architecture | +| `--reco_arch` | `crnn_vgg16_bn` | Recognition architecture | +| `--layout_arch` | `lw_detr_s` | Layout architecture | +| `--device` | `auto` | `auto`, `cpu`, `cuda`, `cuda:1`, `mps`... (`auto` picks a GPU when available) | + +### Processing + +| Argument | Default | Description | +| ------------------------------------------------ | --------- | ---------------------------------------------------------------------- | +| `--assume_straight_pages` / `--no-assume_straight_pages` | on | Handle pages as straight (see [](rotated_documents.md)) | +| `--straighten_pages` | off | Straighten rotated pages before reading them | +| `--preserve_aspect_ratio` / `--no-preserve_aspect_ratio` | on | Pad pages instead of stretching them | +| `--symmetric_pad` / `--no-symmetric_pad` | on | Pad on all sides | +| `--det_bs`, `--reco_bs` | `2`, `128`| Batch sizes of the detection and recognition models | +| `--bin_thresh`, `--box_thresh` | model | [Detection thresholds](ocr_predictor.md#tune-the-text-detection), between 0 and 1 | +| `--confidence_aggregation` | model | `mean`, `min`, `max`, `median`, `geometric_mean` or `harmonic_mean` | +| `--detect_orientation` | off | Estimate the orientation of each page | +| `--detect_language` | off | Estimate the language of each page | +| `--detect_layout` | off | Attach the layout regions to each page | +| `--detect_tables` | off | Recognize tables (enables the layout model) | +| `--ignore_regions CLASS [CLASS ...]` | none | Layout classes to mask before OCR, e.g. `Picture Table` | +| `--export_as_straight_boxes` | off | Export straight boxes with `--no-assume_straight_pages` | +| `--preserve_original_coords` | off | With `--straighten_pages`, map the boxes back to the original pages | +| `--disable_page_orientation` | off | Skip the page orientation classifier | +| `--disable_crop_orientation` | off | Skip the crop orientation classifier | + +### Document assembly + +| Argument | Default | Description | +| ----------------------------------------- | ------- | --------------------------------------------------- | +| `--resolve_lines` / `--no-resolve_lines` | on | Group words into lines | +| `--resolve_blocks` | off | Group lines into blocks | +| `--paragraph_break` | `0.035` | Relative space that splits a line | +| `--keep_reading_order` | off | Sort the content of each page in reading order | + +### Output + +| Argument | Default | Description | +| ------------------------------------------------- | ----------------------------- | --------------------------------------------------------------------------- | +| `--output` | `results.json` | Output file | +| `--format` | from extension | `json`/`dict`, `txt`/`text`, `md`/`markdown`, `adoc`/`asciidoc`, `html`, `xml`/`hocr` | +| `--direction` | `auto` | Reading direction: `auto`, `ltr`, `rtl`, `ttb-rtl`, `ttb-ltr` | +| `--reading_order` / `--no-reading_order` | on | Linearize JSON and hOCR exports in reading order | +| `--escape` / `--no-escape` | on | Escape markup characters (Markdown, AsciiDoc) | +| `--include_furniture` / `--no-include_furniture` | on | Include page headers, footers and footnotes | +| `--images` | `placeholder` | Figures in Markdown, AsciiDoc and HTML: `none`, `placeholder`, `embedded`, `referenced` (see [figures](exporting.md#figures)) | +| `--image_dir` | `_images` | Directory of the figures with `--images referenced` | +| `--image_format` | `png` | `png`, `jpg`, `jpeg` or `webp` | +| `--file_title` | `docTR - XML export (hOCR)` | Title of the hOCR files | +| `--indent` | `4` | Indentation of the JSON export | +| `--quiet` | off | Only log errors | +| `--version` | | Print the docTR version and exit | + +## Output files + +- **JSON** contains the full structure: pages (dimensions, orientation, language, layout), blocks, lines and words + with their confidence and geometry, and the tables with `--detect_tables`. +- **Text-like formats** (`txt`, `md`, `adoc`, `html`) are written to a single file. +- **hOCR** describes one page per file: `--output doc.xml` on a three-page document writes `doc_page_1.xml`, + `doc_page_2.xml` and `doc_page_3.xml`. diff --git a/docs/source/user_guide/exporting.md b/docs/source/user_guide/exporting.md new file mode 100644 index 0000000000..b2e87a8ddf --- /dev/null +++ b/docs/source/user_guide/exporting.md @@ -0,0 +1,210 @@ +# Exporting results + +A {class}`~doctr.io.Document` can be exported to six formats. All of them present the content in the same +**reading order**, which follows columns, places headers and footers, and adapts to right-to-left scripts. + +| Format | Method | Output | Best for | +| ------------- | ------------------------------------------------------- | ---------------------------------------- | ------------------------------------------- | +| Plain text | {meth}`~doctr.io.Document.render` | `str` | Search indexing, NLP, LLM prompts | +| JSON | {meth}`~doctr.io.Document.export` | `dict` | Storing every word with its geometry | +| Markdown | {meth}`~doctr.io.Document.export_as_markdown` | `str` | LLMs, RAG pipelines, readable documents | +| AsciiDoc | {meth}`~doctr.io.Document.export_as_asciidoc` | `str` | Technical documentation toolchains | +| HTML | {meth}`~doctr.io.Document.export_as_html` | `str` | Web display | +| hOCR (XML) | {meth}`~doctr.io.Document.export_as_xml` | `list[tuple[bytes, ElementTree]]` | Searchable PDFs, OCR interoperability | + +The same methods exist on a single {class}`~doctr.io.Page`. The {meth}`~doctr.io.Document.export_as` dispatcher +selects the format by name, which is convenient when it comes from a configuration: + +```python +result.export_as("markdown") # or "md" +result.export_as("asciidoc") # or "adoc" +result.export_as("html") +result.export_as("text") # or "txt", same as render() +result.export_as("json") # or "dict", same as export() +result.export_as("xml") # or "hocr", same as export_as_xml() +``` + +## Plain text + +```python +text = result.render() +``` + +Blocks are separated by a blank line and pages by three blank lines. Both separators are configurable, and page +headers and footers can be left out: + +```python +text = result.render(page_break="\f", include_furniture=False) +``` + +## Markdown, AsciiDoc and HTML + +```python +markdown = result.export_as_markdown() +asciidoc = result.export_as_asciidoc() +html = result.export_as_html() +``` + +The structure of these exports depends on what the predictor detected: + +- **Without layout analysis**, blocks become paragraphs. +- **With layout analysis** (`detect_layout=True`), the layout regions are used to render titles and section + headers as headings, list items as lists, and to place the page furniture (headers, footers, footnotes). +- **With table recognition** (`detect_tables=True`), tables are rendered as real tables. + +::::{tab-set} + +:::{tab-item} Markdown +```markdown +## ACME Office Supplies + +42 Harbour Road, Hamburg + +## Bill to: + +Jane Doe + +12 Market Street, Berlin + +INVOICE + +No. 2026-0412 +Date: 26/10/2026 + +| Description | Qty | Unit price | Amount | +| --- | --- | --- | --- | +| Recycled paper A4 | 10 | 8.50 | 85.00 | +| Ballpoint pens (box) | 5 | 12.00 | 60.00 | +| Desk lamp | 2 | 45.00 | 90.00 | +| Office chair | 1 | 1,015.00 | 1,015.00 | + +Total due + +1,250.00 EUR +``` +::: + +:::{tab-item} HTML +```html +

        ACME Office Supplies

        +

        42 Harbour Road, Hamburg

        +

        Bill to:

        +

        Jane Doe

        +

        12 Market Street, Berlin

        +

        INVOICE

        +

        No. 2026-0412
        +Date: 26/10/2026

        + + + +... +
        DescriptionQtyUnit priceAmount
        Recycled paper A4108.5085.00
        +

        Total due

        +

        1,250.00 EUR

        +``` +::: +:::: + +*Output of `ocr_predictor(pretrained=True, detect_tables=True)` on the +{download}`sample invoice <../_static/images/examples/invoice.pdf>`.* + +Useful options: + +`escape` (Markdown, AsciiDoc) +: Escape the characters that carry a meaning in the markup (`*`, `#`, `|`...). Enabled by default. + +`include_furniture` +: Include page headers, page footers and footnotes. Enabled by default. + +`page_break` +: The separator inserted between pages: `---` in Markdown, `<<<` in AsciiDoc, `
        ` in HTML. + +`images` +: How figures are rendered, see [](#figures) below. + +## hOCR + +[hOCR](https://github.com/kba/hocr-spec) is an open standard to store OCR results in XML. docTR produces one hOCR +document per page, as raw bytes and as an {class}`xml.etree.ElementTree.ElementTree`: + +```python +for page_idx, (xml_bytes, xml_tree) in enumerate(result.export_as_xml()): + with open(f"page_{page_idx + 1}.hocr", "wb") as f: + f.write(xml_bytes) +``` + +hOCR files are the input of tools such as [hocr-tools](https://github.com/ocropus/hocr-tools) or +[OCRmyPDF](https://ocrmypdf.readthedocs.io). The +[Export as PDF/A notebook](https://github.com/mindee/notebooks/blob/main/doctr/export_as_pdfa.ipynb) shows how to +turn docTR results into **searchable PDFs**. + +## Reading order + +Every export path shares the same linearization of the page, computed once and memoized. It: + +- follows the **columns** of multi-column layouts instead of reading straight across the page, +- infers the **reading direction** from the recognized text (right-to-left for Arabic or Hebrew, for instance), +- uses the **layout regions**, when available, to order blocks, headings, tables and figures and to place the + page furniture. + +Force the direction when automatic detection is not what you want: + +```python +result.export_as_markdown(direction="rtl") # 'auto', 'ltr', 'rtl', 'ttb-rtl' or 'ttb-ltr' +``` + +To get the elements of a page (blocks, tables and optionally figures) in reading order, for your own exporter: + +```python +for item in result.pages[0].items_in_reading_order(include_figures=True): + print(type(item).__name__, item.render()[:40]) +``` + +The JSON export follows the reading order by default (`export(reading_order=False)` keeps the detection order). To +sort the `Document` tree itself, build the predictor with `keep_reading_order=True`. + +## Figures + +When the layout model runs (`detect_layout=True` or `detect_tables=True`), the regions it labels as figures +(pictures, charts...) take part in the reading order. The Markdown, AsciiDoc and HTML exports render them according +to their `images` argument: + +| Mode | Rendering | +| ---------------------------- | ----------------------------------------------------------------------------------------- | +| `"placeholder"` *(default)* | A comment marks the figure: `` (Markdown, HTML) or `// image` (AsciiDoc) | +| `"none"` | Figures are left out | +| `"embedded"` | Each figure is cropped and inlined as a base64 data URI: the export is self-contained | +| `"referenced"` | Each crop is written to a directory and linked by its relative path | + +```python +from doctr.io import DocumentFile, FigureEncoder +from doctr.models import ocr_predictor + +predictor = ocr_predictor(pretrained=True, detect_layout=True) +result = predictor(DocumentFile.from_pdf("report.pdf")) + +# A self-contained Markdown file, figures included +markdown = result.export_as_markdown(images="embedded") + +# A Markdown file linking to the crops written to `assets/` +markdown = result.export_as_markdown(images=FigureEncoder("referenced", image_dir="assets")) +``` + +{class}`~doctr.io.FigureEncoder` also sets the image format (`png`, `jpg`, `webp`), the quality of lossy formats, +a padding around each crop, and a `path_prefix` when the export is not written next to the image directory. +Crop file names contain the position and a hash of the figure (e.g. `page1_figure2-3fa2b1c9.png`), so several +documents can share a directory. + +:::{note} +Once a figure carries its pixels (`"embedded"` and `"referenced"`), the text recognized inside it is left out of +the export, since the image shows it, and its caption becomes the alternative text of the image. Plain text never +contains figures, and hOCR positions them as `ocr_photo` areas. +::: + +## From the command line + +The [CLI](cli.md) exposes all these formats; the format is inferred from the extension of the output file: + +```bash +doctr-cli --input_path report.pdf --detect_layout --output report.md --images referenced +``` diff --git a/docs/source/user_guide/faq.md b/docs/source/user_guide/faq.md new file mode 100644 index 0000000000..80e3f9b2df --- /dev/null +++ b/docs/source/user_guide/faq.md @@ -0,0 +1,112 @@ +# Troubleshooting & FAQ + +## Installation and setup + +:::{dropdown} `ModuleNotFoundError: No module named 'matplotlib'` when calling `.show()` +:open: + +Visualization relies on optional dependencies. Install the `viz` extra: + +```bash +pip install "python-doctr[viz]" +``` + +Similarly, `DocumentFile.from_url` needs the `html` extra and the `contrib` module needs the `contrib` extra +(see [](../getting_started/installation.md#optional-extras)). +::: + +:::{dropdown} The pretrained weights cannot be downloaded +Weights are downloaded on first use. Behind a proxy or on a machine without internet access, download them once +on a connected machine and copy the cache directory (`~/.cache/doctr` by default), or point `DOCTR_CACHE_DIR` to +a shared location. See [](../deployment/configuration.md). +::: + +:::{dropdown} Errors in a restricted environment (AWS Lambda, read-only file systems) +docTR writes its model cache to `~/.cache/doctr` and uses multiprocessing for some data operations. Set +`DOCTR_CACHE_DIR` to a writable directory and `DOCTR_MULTIPROCESSING_DISABLE=TRUE` +(see [](../deployment/configuration.md#aws-lambda)). +::: + +## Inputs + +:::{dropdown} `ValueError: incorrect input shape` +Predictors expect a **list** of pages, each a 3-channel array of shape `(H, W, 3)`. Wrap a single page in a list +(`predictor([page])`) and convert grayscale images to 3 channels: + +```python +import numpy as np + +page = np.stack([gray] * 3, axis=-1) +``` +::: + +:::{dropdown} The results are wrong on images loaded with OpenCV +OpenCV loads images in **BGR** order, while docTR expects **RGB**. Convert them first, or use +`DocumentFile.from_images`, which handles it for you: + +```python +import cv2 + +page = cv2.cvtColor(cv2.imread("scan.jpg"), cv2.COLOR_BGR2RGB) +``` +::: + +:::{dropdown} Does docTR use the text layer of digital PDFs? +No: every page is rendered to an image and read by the models, so scanned and digital-born PDFs are processed the +same way. If your PDFs have a reliable text layer and you only need the text, a PDF text extractor is faster. +::: + +## Accuracy + +:::{dropdown} Some words are missing +- Small print: render PDFs at a higher resolution, e.g. `DocumentFile.from_pdf(path, scale=3)`. +- Faint or low-contrast text: lower the detection thresholds (`bin_thresh`, `box_thresh`), see + [](ocr_predictor.md#tune-the-text-detection). +- Very large images: keep `preserve_aspect_ratio=True` (default) so the page is not distorted. +::: + +:::{dropdown} The output contains odd characters (accents, symbols) that are not in my documents +Restrict the recognition model to the characters you expect with a whitelist, see +[](ocr_predictor.md#restrict-the-output-characters). +::: + +:::{dropdown} My documents are in a language with another alphabet +The pretrained recognition models are trained on the `french` vocabulary (Latin script). For other scripts, look for +a [community model](../training/sharing_models.md#community-models) on the Hugging Face Hub, or +[train a model](../training/train.md) on a matching [vocabulary](../models/vocabs.md). +::: + +:::{dropdown} The text is read in the wrong order +Use an export method (`render()`, Markdown, HTML...), which applies the reading-order algorithm, rather than +iterating the blocks. Enable `detect_layout=True` for complex layouts, and set `direction` if the automatic +detection fails. See [](exporting.md#reading-order). +::: + +:::{dropdown} Rotated pages give poor results +The default mode assumes upright pages. Use `straighten_pages=True` or `assume_straight_pages=False` +(see [](rotated_documents.md)). +::: + +## Performance + +:::{dropdown} OCR is slow +- Run on a GPU, and use half precision (see [](../deployment/performance.md)). +- Create the predictor **once** and reuse it. +- Pass several pages per call and increase `det_bs` / `reco_bs` if your hardware allows it. +- Try lighter architectures (`fast_tiny`, `crnn_mobilenet_v3_small`), see the [model zoo](../models/index.md). +- Keep `assume_straight_pages=True` when your pages are upright. +::: + +:::{dropdown} I run out of GPU memory +Lower the batch sizes (`det_bs`, `reco_bs`), process fewer pages per call, or render PDFs at a lower `scale`. +::: + +## Still stuck? + +Search the [GitHub issues](https://github.com/mindee/doctr/issues) and +[discussions](https://github.com/mindee/doctr/discussions), or ask the community (see [](../community/index.md)). +When opening an issue, include the output of: + +```bash +python scripts/collect_env.py +``` diff --git a/docs/source/user_guide/index.md b/docs/source/user_guide/index.md new file mode 100644 index 0000000000..8c09b7a4b7 --- /dev/null +++ b/docs/source/user_guide/index.md @@ -0,0 +1,124 @@ +# User guide + +The user guide explains how to get the most out of docTR on your own documents, one topic at a time. Each page +is self-contained and full of examples you can copy. + +## The essentials + +::::{grid} 1 2 2 2 +:gutter: 3 + +:::{grid-item-card} {octicon}`file;1.1em` Loading documents +:link: loading_documents +:link-type: doc + +Read PDFs, images, web pages and in-memory data into pages. +::: + +:::{grid-item-card} {octicon}`gear;1.1em` Running OCR +:link: ocr_predictor +:link-type: doc + +Choose the models and configure the end-to-end OCR predictor. +::: + +:::{grid-item-card} {octicon}`list-unordered;1.1em` Working with results +:link: results +:link-type: doc + +Navigate the `Document`, convert coordinates, visualize and save predictions. +::: + +:::{grid-item-card} {octicon}`file-code;1.1em` Exporting results +:link: exporting +:link-type: doc + +Text, JSON, Markdown, AsciiDoc, HTML and hOCR, in reading order. +::: +:::: + +## Beyond plain text + +::::{grid} 1 2 2 2 +:gutter: 3 + +:::{grid-item-card} {octicon}`table;1.1em` Layout analysis & tables +:link: layout_and_tables +:link-type: doc + +Detect titles, figures and tables, and extract tables as row/column grids. +::: + +:::{grid-item-card} {octicon}`sync;1.1em` Rotated documents +:link: rotated_documents +:link-type: doc + +Read skewed scans, photos and pages in any orientation. +::: + +:::{grid-item-card} {octicon}`tag;1.1em` Key information extraction +:link: kie +:link-type: doc + +Detect several classes of fields (dates, amounts...) and read them in one pass. +::: + +:::{grid-item-card} {octicon}`package;1.1em` Task-specific predictors +:link: standalone_predictors +:link-type: doc + +Use the detection, recognition, layout, table and orientation models on their own. +::: + +:::{grid-item-card} {octicon}`image;1.1em` Artefact detection +:link: artefacts +:link-type: doc + +Find logos, QR codes, barcodes and photos with the `contrib` module. +::: + +:::{grid-item-card} {octicon}`terminal;1.1em` Command-line interface +:link: cli +:link-type: doc + +Run OCR on files from your terminal with `doctr-cli`. +::: +:::: + +## When something goes wrong + +:::{card} {octicon}`question;1.1em` Troubleshooting & FAQ +:link: faq +:link-type: doc + +Solutions to the most common installation, input and accuracy issues. +::: + +```{toctree} +:hidden: +:caption: The essentials + +loading_documents +ocr_predictor +results +exporting +``` + +```{toctree} +:hidden: +:caption: Beyond plain text + +layout_and_tables +rotated_documents +kie +standalone_predictors +artefacts +cli +``` + +```{toctree} +:hidden: +:caption: Help + +faq +``` diff --git a/docs/source/user_guide/kie.md b/docs/source/user_guide/kie.md new file mode 100644 index 0000000000..5587d1ff7f --- /dev/null +++ b/docs/source/user_guide/kie.md @@ -0,0 +1,73 @@ +# Key information extraction + +The OCR predictor finds *all* the words of a page. Often you only care about a few fields: the date, the total +amount, the invoice number... The **KIE predictor** (Key Information Extraction) uses a detection model trained on +several classes, so that each detected box comes with the *type* of information it contains. + +## How it differs from OCR + +| | {func}`~doctr.models.ocr_predictor` | {func}`~doctr.models.kie_predictor` | +| --- | --- | --- | +| Detection model | One class: `words` | Any number of classes (`date`, `total`, `address`...) | +| Output | {class}`~doctr.io.Document` of pages, blocks, lines and words | {class}`~doctr.io.KIEDocument` of pages with predictions grouped **by class** | +| Typical use | Read everything | Extract specific fields | + +## Run a KIE predictor + +The KIE predictor takes the same arguments as the OCR predictor (architectures, orientation, layout...): + +```python +from doctr.io import DocumentFile +from doctr.models import kie_predictor + +predictor = kie_predictor(det_arch="db_resnet50", reco_arch="crnn_vgg16_bn", pretrained=True) +result = predictor(DocumentFile.from_pdf("invoice.pdf")) + +page = result.pages[0] +for class_name, predictions in page.predictions.items(): + for prediction in predictions: + print(class_name, prediction.value, prediction.confidence, prediction.geometry) +``` + +Each {class}`~doctr.io.Prediction` is a word with an extra `crop_orientation` field. `render()` and the exports +group the predictions by class: + +```python +print(result.render()) +# words: ACME +# +# words: Office +# ... +``` + +:::{important} +The pretrained detection models only know the `words` class, so out of the box the KIE predictor behaves like the +OCR predictor with a flat output. Its value comes with a detection model **trained on your own classes**. +::: + +## Use your own classes + +1. Annotate your documents with one polygon list per class (see the + [multi-class detection format](../training/train.md#text-detection)), e.g. with the + [docTR Labeler](../community/tools.md#doctr-labeler). +2. Train a detection model with these classes using the + [detection training script](../training/train.md#text-detection). +3. Load it with its class names and plug it into the KIE predictor: + +```python +from doctr.models import db_resnet50, kie_predictor + +det_model = db_resnet50(pretrained=False, pretrained_backbone=False, class_names=["date", "total", "invoice_id"]) +det_model.from_pretrained("path/to/your/checkpoint.pt") + +predictor = kie_predictor(det_arch=det_model, reco_arch="crnn_vgg16_bn", pretrained=True) +result = predictor(pages) + +dates = [prediction.value for prediction in result.pages[0].predictions["date"]] +``` + +## Visualize and export + +{class}`~doctr.io.KIEDocument` offers the same tools as `Document`: `show()` draws the boxes with one color per +class, `export()` returns a JSON-ready dict with a `predictions` key per page, and the +[export formats](exporting.md) are available as well. diff --git a/docs/source/user_guide/layout_and_tables.md b/docs/source/user_guide/layout_and_tables.md new file mode 100644 index 0000000000..5ad7ece8ff --- /dev/null +++ b/docs/source/user_guide/layout_and_tables.md @@ -0,0 +1,177 @@ +# Layout analysis & tables + +Text alone loses the structure of a document. docTR can also tell *what* each region of a page is (a title, a +paragraph, a table, a figure...) and rebuild tables as rows and columns. + +## Layout analysis + +A layout model detects and classifies the regions of a page. The pretrained `lw_detr_s` model recognizes +11 classes: + +{bdg-primary-line}`Title` {bdg-primary-line}`Section-header` {bdg-primary-line}`Text` {bdg-primary-line}`List-item` +{bdg-primary-line}`Caption` {bdg-primary-line}`Footnote` {bdg-primary-line}`Formula` {bdg-primary-line}`Table` +{bdg-primary-line}`Picture` {bdg-primary-line}`Page-header` {bdg-primary-line}`Page-footer` + +### In the OCR pipeline + +Pass `detect_layout=True` and the regions are attached to every page as +{class}`~doctr.io.LayoutElement` objects: + +```python +from doctr.io import DocumentFile +from doctr.models import ocr_predictor + +predictor = ocr_predictor(pretrained=True, detect_layout=True) +result = predictor(DocumentFile.from_pdf("invoice.pdf")) + +for region in result.pages[0].layout: + print(f"{region.type:<15} {region.confidence:.2f}") +``` + +```text +Page-footer 0.89 +Section-header 0.75 +Section-header 0.67 +Table 0.95 +Text 0.78 +... +``` + +```{image} ../_static/images/examples/invoice_layout.png +:alt: Words and layout regions overlaid on the invoice +:width: 520px +:align: center +``` + +The layout also improves the exports: headings, lists, tables, figures, headers and footers are rendered with the +right structure and in the right place (see [](exporting.md)). It is part of the JSON export under the `layout` +key, and `page.show()` draws it. + +The layout model shares the input settings of the predictor (`assume_straight_pages`, `preserve_aspect_ratio`...) +and the batch size of the detection model (`det_bs`). Use `layout_arch` to select another architecture, or to pass +[your own fine-tuned model](../training/custom_models.md#layout-models). + +### Ignore some regions + +Pictures, stamps or tables can produce noisy text. `ignore_regions` masks the regions of the given layout classes +**before** text detection and recognition, so their content is simply not read: + +```python +predictor = ocr_predictor(pretrained=True, detect_layout=True, ignore_regions=["Picture", "Page-footer"]) +``` + +:::{important} +`ignore_regions` relies on the layout model: enable it with `detect_layout=True` (or `detect_tables=True`), +otherwise the regions cannot be located and nothing is masked. The CLI enables it automatically. +::: + +## Table structure recognition + +With `detect_tables=True`, each region the layout model labels as a `Table` is cropped and passed to a table +structure model (`tablecenternet`), which predicts its cells and the rows and columns they span. The words falling +inside a cell are regrouped into a {class}`~doctr.io.Table`: + +```python +from doctr.io import DocumentFile +from doctr.models import ocr_predictor + +predictor = ocr_predictor(pretrained=True, detect_tables=True) +result = predictor(DocumentFile.from_pdf("invoice.pdf")) + +table = result.pages[0].tables[0] +print(table.num_rows, table.num_cols) # 5 4 +print(table.render()) +``` + +```text +Description Qty Unit price Amount +Recycled paper A4 10 8.50 85.00 +Ballpoint pens (box) 5 12.00 60.00 +Desk lamp 2 45.00 90.00 +Office chair 1 1,015.00 1,015.00 +``` + +:::{important} +The words of a table are **removed from the regular `blocks`** of the page, so the same text is not returned +twice. Read them through `page.tables`, or use an export: tables are rendered in place by `render()`, Markdown, +AsciiDoc and HTML, and stored under the `tables` key of the JSON export. +::: + +### Load a table into pandas + +{meth}`~doctr.io.Table.to_grid` returns a dense `num_rows` × `num_cols` grid of strings. A cell spanning several +rows or columns has its value at its top-left position: + +```python +import pandas as pd + +grid = table.to_grid() +df = pd.DataFrame(grid[1:], columns=grid[0]) # first row as header +print(df) +``` + +```text + Description Qty Unit price Amount +0 Recycled paper A4 10 8.50 85.00 +1 Ballpoint pens (box) 5 12.00 60.00 +2 Desk lamp 2 45.00 90.00 +3 Office chair 1 1,015.00 1,015.00 +``` + +### Cells, spans and geometry + +For full control, iterate over the {class}`~doctr.io.TableCell` objects: + +```python +for cell in table.cells: + print(cell.value, (cell.row_start, cell.row_end), (cell.col_start, cell.col_end), cell.geometry) +``` + +| Attribute | Description | +| ---------------------------- | ------------------------------------------------------------------- | +| `value` | The text of the words assigned to the cell | +| `row_start`, `row_end` | First and last row spanned by the cell (0-indexed, inclusive) | +| `col_start`, `col_end` | First and last column spanned by the cell (0-indexed, inclusive) | +| `row_span`, `col_span` | Number of rows / columns spanned | +| `geometry` | Position of the cell, relative to the page | +| `confidence` | Mean recognition confidence of the words of the cell | +| `objectness_score` | Score of the cell predicted by the table model | + +## Standalone predictors + +Both models can also run on their own, without text recognition: + +::::{tab-set} + +:::{tab-item} Layout +```python +from doctr.models import layout_predictor + +predictor = layout_predictor("lw_detr_s", pretrained=True) +out = predictor(pages) + +# One dict per page +out[0]["class_names"] # ['Page-footer', 'Section-header', ..., 'Table', 'Text', ...] +out[0]["boxes"] # (N, 4) array of relative (xmin, ymin, xmax, ymax) +out[0]["scores"] # N confidence scores +``` +::: + +:::{tab-item} Table structure +Pass images of tables, e.g. crops of the `Table` regions: + +```python +from doctr.models import table_predictor + +predictor = table_predictor("tablecenternet", pretrained=True) +out = predictor([table_image]) + +out[0]["num_rows"], out[0]["num_cols"] # (5, 4) +out[0]["cells"][0] +# {'geometry': [[x1, y1], ..., [x4, y4]], # relative to the table image +# 'score': 0.42, 'row_start': 0, 'row_end': 0, 'col_start': 0, 'col_end': 0} +``` +::: +:::: + +See [](standalone_predictors.md) for all the task-specific predictors. diff --git a/docs/source/user_guide/loading_documents.md b/docs/source/user_guide/loading_documents.md new file mode 100644 index 0000000000..5798f20eb6 --- /dev/null +++ b/docs/source/user_guide/loading_documents.md @@ -0,0 +1,131 @@ +# Loading documents + +Predictors work on **pages**: a list of NumPy arrays of shape `(H, W, 3)`, in RGB order and `uint8` type. +{class}`~doctr.io.DocumentFile` reads PDFs, images and web pages into that format, so you rarely have to think +about it. + +## From a PDF + +```python +from doctr.io import DocumentFile + +pages = DocumentFile.from_pdf("path/to/report.pdf") +print(len(pages), pages[0].shape) # e.g. 3 (2339, 1653, 3) +``` + +Every page of the PDF is rendered to an image (with [pypdfium2](https://pypdfium2.readthedocs.io)), so +`pages[i]` and `result.pages[i]` always correspond to the *i*-th page of the file. + +::::{tab-set} + +:::{tab-item} Rendering resolution +The `scale` parameter controls the rendering resolution, where `1` corresponds to 72 dpi. The default, `2` +(144 dpi), is a good trade-off for most documents; increase it for small print: + +```python +pages = DocumentFile.from_pdf("path/to/report.pdf", scale=3) # 216 dpi +``` +::: + +:::{tab-item} Encrypted PDF +```python +pages = DocumentFile.from_pdf("path/to/protected.pdf", password="s3cr3t") +``` +::: + +:::{tab-item} From memory +Pass the raw bytes, e.g. a file received by a web API or downloaded from object storage: + +```python +with open("path/to/report.pdf", "rb") as f: + pages = DocumentFile.from_pdf(f.read()) +``` +::: +:::: + +:::{tip} +docTR always runs OCR on the rendered pages, even when the PDF already contains a text layer. This gives +consistent results on scanned and digital-born PDFs alike. +::: + +## From images + +Any format supported by OpenCV works (PNG, JPEG, TIFF, BMP, WebP...). Pass one path, or several to build a +multi-page document: + +```python +from doctr.io import DocumentFile + +# A single image is a one-page document +pages = DocumentFile.from_images("path/to/scan.jpg") + +# Several images are read as consecutive pages of the same document +pages = DocumentFile.from_images(["path/to/page1.png", "path/to/page2.png"]) + +# Raw bytes work too +with open("path/to/scan.jpg", "rb") as f: + pages = DocumentFile.from_images(f.read()) +``` + +:::{note} +A multi-frame TIFF is read as a single image (its first frame). Split it into frames first, e.g. with Pillow, +and pass them as NumPy arrays (see below). +::: + +## From a web page + +Web pages are first printed to PDF with [WeasyPrint](https://weasyprint.org), so this requires the `html` extra +(`pip install "python-doctr[html]"`): + +```python +from doctr.io import DocumentFile + +pages = DocumentFile.from_url("https://www.example.com") +``` + +## From NumPy arrays + +If your images are already in memory, e.g. frames from a camera or pages produced by another library, +pass them directly to the predictor as a list of arrays: + +```python +import numpy as np +from PIL import Image + +from doctr.models import ocr_predictor + +predictor = ocr_predictor(pretrained=True) + +page = np.asarray(Image.open("path/to/scan.png").convert("RGB")) # (H, W, 3), uint8, RGB +result = predictor([page]) +``` + +:::{warning} +Make sure arrays are **RGB** (OpenCV loads images as BGR: convert them with `cv2.cvtColor(img, cv2.COLOR_BGR2RGB)`), +of type `uint8`, and have **3 channels** (convert grayscale images with `np.stack([img] * 3, axis=-1)`). +::: + +## Mixing sources + +`DocumentFile` methods return plain Python lists, so you can concatenate them into a single document: + +```python +pages = DocumentFile.from_pdf("cover_letter.pdf") + DocumentFile.from_images(["id_front.jpg", "id_back.jpg"]) +result = predictor(pages) # one Document with all the pages, in order +``` + +## Low-level readers + +`DocumentFile` is a thin wrapper around these functions, which you can use directly when you need more control: + +| Function | Reads | +| --------------------------------------------- | --------------------------------------------------------- | +| {func}`~doctr.io.read_pdf` | A PDF into a list of arrays (`scale`, `password`, `rgb_mode`) | +| {func}`~doctr.io.read_img_as_numpy` | One image into an array (optionally resized) | +| {func}`~doctr.io.read_img_as_tensor` | One image into a `torch.Tensor` | +| {func}`~doctr.io.decode_img_as_tensor` | Image bytes into a `torch.Tensor` | +| {func}`~doctr.io.read_html` | A web page into PDF bytes | + +## Next steps + +Your pages are ready: head to [](ocr_predictor.md) to configure the predictor that will read them. diff --git a/docs/source/user_guide/ocr_predictor.md b/docs/source/user_guide/ocr_predictor.md new file mode 100644 index 0000000000..f43b950039 --- /dev/null +++ b/docs/source/user_guide/ocr_predictor.md @@ -0,0 +1,323 @@ +# Running OCR + +{func}`~doctr.models.ocr_predictor` builds the end-to-end pipeline: it detects the words of each page, reads them, +and assembles the result into a {class}`~doctr.io.Document`. This page walks through its options, from choosing +the models to fine-tuning the output. + +## Create a predictor + +```python +from doctr.io import DocumentFile +from doctr.models import ocr_predictor + +predictor = ocr_predictor(pretrained=True) + +pages = DocumentFile.from_pdf("path/to/report.pdf") +result = predictor(pages) +``` + +Building a predictor loads the models (and downloads their weights the first time), so **create it once and reuse +it** for all your documents. The predictor runs in inference mode and accepts any number of pages per call. + +## Choose the architectures + +By default, `ocr_predictor` uses `fast_base` for text detection and `crnn_vgg16_bn` for text recognition. Pass the +names of other architectures to trade speed for accuracy: + +```python +predictor = ocr_predictor(det_arch="db_resnet50", reco_arch="parseq", pretrained=True) +``` + +| You want... | Detection (`det_arch`) | Recognition (`reco_arch`) | +| ---------------------------------- | --------------------------------------- | ------------------------------------------------ | +| A good default | `fast_base` | `crnn_vgg16_bn` | +| Maximum speed (CPU, edge devices) | `fast_tiny`, `db_mobilenet_v3_large` | `crnn_mobilenet_v3_small`, `viptr_tiny` | +| Maximum accuracy | `db_resnet50`, `fast_base` | `parseq`, `vitstr_base`, `master` | + +The [model zoo](../models/index.md) lists every architecture with its benchmark. Instead of a name, you can also pass +a model **instance**, which is how you plug in [your own trained models](../training/custom_models.md): + +```python +from doctr.models import ocr_predictor, fast_base, parseq + +predictor = ocr_predictor( + det_arch=fast_base(pretrained=True), + reco_arch=parseq(pretrained=True), +) +``` + +## Options at a glance + +All options are keyword arguments of {func}`~doctr.models.ocr_predictor`. + +::::{tab-set} + +:::{tab-item} Page geometry +| Option | Default | Effect | +| -------------------------- | ------- | -------------------------------------------------------------------------------------------------------- | +| `assume_straight_pages` | `True` | Assume upright pages with horizontal text: fastest, returns straight boxes. Set to `False` for rotated or skewed documents. | +| `straighten_pages` | `False` | Estimate the page rotation and straighten the pages before running the models | +| `export_as_straight_boxes` | `False` | With `assume_straight_pages=False`, still export straight boxes instead of polygons | +| `preserve_aspect_ratio` | `True` | Pad the pages instead of stretching them when resizing them for the models | +| `symmetric_pad` | `True` | With `preserve_aspect_ratio=True`, pad on all sides instead of bottom-right only | + +See [](rotated_documents.md) for a detailed guide. +::: + +:::{tab-item} Extra predictions +| Option | Default | Effect | +| -------------------- | ------------ | --------------------------------------------------------------------------------------------------- | +| `detect_orientation` | `False` | Estimate the general orientation of each page, available as `page.orientation` | +| `detect_language` | `False` | Estimate the language of each page, available as `page.language` | +| `detect_layout` | `False` | Run a layout model and attach its regions to each page (`page.layout`) | +| `layout_arch` | `"lw_detr_s"`| Layout architecture name or model instance | +| `detect_tables` | `False` | Recognize the structure of tables (`page.tables`); enables the layout model | +| `ignore_regions` | `None` | Layout classes to mask out before OCR, e.g. `["Picture"]`; requires the layout model (`detect_layout=True`) | + +See [](layout_and_tables.md). +::: + +:::{tab-item} Output structure +| Option | Default | Effect | +| -------------------- | ------- | --------------------------------------------------------------------------------------- | +| `resolve_lines` | `True` | Group words into lines | +| `resolve_blocks` | `False` | Group lines into blocks (by default, all the lines of a page form a single block) | +| `paragraph_break` | `0.035` | Minimum horizontal gap, relative to the page width, that splits two words into different lines | +| `keep_reading_order` | `False` | Sort the blocks of every page in reading order | +::: + +:::{tab-item} Performance +| Option | Default | Effect | +| -------------------------- | ------- | --------------------------------------------------------------------------------------- | +| `det_bs` | `2` | Batch size of the detection model (also used by the layout and table models) | +| `reco_bs` | `128` | Batch size of the recognition model (number of word crops per batch) | +| `disable_page_orientation` | `False` | Skip the page orientation classifier (see [rotated documents](rotated_documents.md)) | +| `disable_crop_orientation` | `False` | Skip the word crop orientation classifier | + +See [](../deployment/performance.md) for GPU and half-precision inference. +::: +:::: + +## Tune the text detection + +The detection models output a probability map that is binarized, then split into boxes. Two thresholds of the +post-processor control this step: + +`bin_thresh` +: Minimum probability for a pixel to be considered as text. Lower it to catch faint text, raise it to separate words + that are close to each other. + +`box_thresh` +: Minimum mean score of a box to be kept. Lower it to keep more (uncertain) words, raise it to drop noise. + +```python +predictor = ocr_predictor(pretrained=True) + +predictor.det_predictor.model.postprocessor.bin_thresh = 0.3 +predictor.det_predictor.model.postprocessor.box_thresh = 0.1 +``` + +## Confidence scores + +The recognition model predicts a probability for each character of a word. They are aggregated into +`word.confidence` with one of the following methods: + +| Method | Behavior | +| ------------------------------------- | ----------------------------------------------------------------------------------- | +| `"min"` | The least confident character (default for CRNN, VIPTR, SAR and MASTER) | +| `"mean"` | Average probability (default for ViTSTR and PARSeq) | +| `"geometric_mean"`, `"harmonic_mean"` | Means that penalize a single uncertain character more than the arithmetic mean | +| `"median"`, `"max"` | Ignore most uncertain characters: they barely separate misread words from correct ones | +| a callable | Any function taking the 1D array of probabilities and returning a float | + +For the probabilities `[0.99, 0.98, 0.12, 0.99]` (one doubtful character), the mean is `0.77`, the geometric mean +`0.58`, the harmonic mean `0.35` and the minimum `0.12`. Pick a method that makes uncertain words stand out if +you use the confidence to flag words for human review. + +```python +from doctr.models import ocr_predictor, parseq + +# When building the predictor (modifies a model instance passed as `reco_arch`) +predictor = ocr_predictor(pretrained=True, confidence_aggregation="geometric_mean") + +# Or afterwards, on the post-processor of the recognition model +predictor.reco_predictor.model.postprocessor.confidence_aggregation = "mean" + +# Or on a model instance +predictor = ocr_predictor(reco_arch=parseq(pretrained=True, confidence_aggregation="min"), pretrained=True) +``` + +:::{dropdown} Details: which probabilities are aggregated? +Only the probabilities of the predicted characters: the CTC blank frames (CRNN, VIPTR) and the positions from the +end-of-sequence token onwards (SAR, MASTER) are ignored, and a CTC character has the highest probability of its +frames. An empty prediction has a confidence of `0`. + +Very wide crops are split into parts that are read separately; their confidences are combined with the +`split_confidence_aggregation` of the recognition predictor (`"min"` by default): + +```python +predictor.reco_predictor.split_confidence_aggregation = "mean" +``` +::: + +## Restrict the output characters + +If you know which languages to expect, restrict the recognition model to their characters with +{func}`~doctr.models.utils.add_whitelist`. Forbidden characters can no longer be predicted, which removes a whole +class of errors (e.g. `ł` read instead of `l` on an English document). + +```python +from doctr.datasets import VOCABS +from doctr.models import ocr_predictor +from doctr.models.utils import add_whitelist + +predictor = ocr_predictor(pretrained=True) + +# Only German and Polish characters can be predicted from now on +handle = add_whitelist(predictor, [VOCABS["german"], VOCABS["polish"]]) +result = predictor(pages) + +# Restore the original decoding +handle.remove() +``` + +The handle is also a context manager, which removes the whitelist when the block ends: + +```python +with add_whitelist(predictor, VOCABS["german"]): + result = predictor(pages) +``` + +By default, forbidden characters are dropped (`strategy="mask"`) and the model falls back to its best allowed +character. With `strategy="nearest"`, each forbidden character is first folded onto the closest allowed one, +which is handy to normalize accents onto a base alphabet: + +```python +# é -> e, ł -> l, ß -> ss...: an ASCII-only output +handle = add_whitelist(predictor, VOCABS["latin"], strategy="nearest") +``` + +The closest character is found by transliteration by default. Pass `mapping="weights"` to derive it from the +model's own confusions instead, or a `{"forbidden": "allowed"}` dict to override specific characters. + +:::{note} +A whitelist can only *restrict* a model: characters it was not trained on are silently ignored. To read a new +alphabet, use a model trained on a matching [vocabulary](../models/vocabs.md). +::: + +## Hooks + +Hooks let you edit the detected boxes **before they are read**. A hook is a callable that receives the location +predictions right after text detection and returns them, possibly modified. The recognition model then reads the +crops of the boxes the hook returned: + +- **remove** boxes to skip regions you don't need (they are not read, which also saves time); +- **move or resize** boxes, e.g. to pad crops that are too tight; +- **add** boxes, e.g. for fields at a known position on a form. + +The location predictions are a list with one NumPy array per page, in relative coordinates: `(N, 4)` straight boxes +as `xmin, ymin, xmax, ymax`, or `(N, 4, 2)` polygons with `assume_straight_pages=False`. A hook returns the same +structure, with any number of boxes per page. + +::::{tab-set} + +:::{tab-item} Skip headers & footers +```python +import numpy as np + +from doctr.models import ocr_predictor + + +class SkipMargins: + """Ignore the words in the top and bottom bands of the page (headers, footers)""" + + def __init__(self, band: float = 0.05): + self.band = band + + def __call__(self, loc_preds: list[np.ndarray]) -> list[np.ndarray]: + kept = [] + for boxes in loc_preds: # one array per page, relative coordinates + if boxes.ndim == 2: # straight boxes (N, 4): xmin, ymin, xmax, ymax + center_y = (boxes[:, 1] + boxes[:, 3]) / 2 + else: # rotated boxes (N, 4, 2): polygons + center_y = boxes[:, :, 1].mean(axis=1) + keep = (center_y > self.band) & (center_y < 1 - self.band) + kept.append(boxes[keep]) + return kept + + +predictor = ocr_predictor(pretrained=True) +predictor.add_hook(SkipMargins()) +``` + +On the {download}`sample invoice <../_static/images/examples/invoice.pdf>`, the footer line +(`ACME Office Supplies GmbH - VAT ID ...`) is no longer read. +::: + +:::{tab-item} Pad the boxes +```python +import numpy as np + +from doctr.models import ocr_predictor + + +def pad_boxes(loc_preds: list[np.ndarray], pad: float = 0.002) -> list[np.ndarray]: + """Enlarge the straight boxes slightly, so that the crops keep the character edges""" + offsets = np.array([-pad, -pad, pad, pad]) + return [np.clip(boxes + offsets.astype(boxes.dtype), 0, 1) for boxes in loc_preds] + + +predictor = ocr_predictor(pretrained=True) +predictor.add_hook(pad_boxes) +``` +::: +:::: + +Good to know: + +- Hooks run in the order they were added, each one receiving the output of the previous one. +- Each returned box keeps the `objectness_score` of the closest detected box of its page; a box added on a page + without any detection gets a score of `1`. +- With the [KIE predictor](kie.md), hooks receive and return a dict with one such list per class. +- An output in the wrong format (not one array per page, or boxes of another shape) raises a `ValueError`. + +## Process many documents + +- **Reuse the predictor**: building it is the expensive part. +- **Batch your pages**: one call on a list of pages is faster than one call per page, as the models process + several pages and word crops at once (see `det_bs` and `reco_bs` above). +- **Free memory between documents**: the predictor returns plain Python objects, so drop the results you no + longer need. + +```python +from pathlib import Path + +from doctr.io import DocumentFile +from doctr.models import ocr_predictor + +predictor = ocr_predictor(pretrained=True) + +for path in sorted(Path("invoices").glob("*.pdf")): + result = predictor(DocumentFile.from_pdf(path)) + path.with_suffix(".txt").write_text(result.render()) +``` + +## Next steps + +::::{grid} 1 2 2 2 +:gutter: 3 + +:::{grid-item-card} {octicon}`list-unordered;1.1em` Working with results +:link: results +:link-type: doc + +Navigate the `Document`, read words and boxes, and save the predictions. +::: + +:::{grid-item-card} {octicon}`rocket;1.1em` Performance +:link: ../deployment/performance +:link-type: doc + +Run on GPU, in half precision, and compile the models. +::: +:::: diff --git a/docs/source/user_guide/results.md b/docs/source/user_guide/results.md new file mode 100644 index 0000000000..93978ccac4 --- /dev/null +++ b/docs/source/user_guide/results.md @@ -0,0 +1,234 @@ +# Working with results + +The OCR predictor returns a {class}`~doctr.io.Document`. This page shows how to navigate it, turn its geometries +into pixels, visualize the predictions, and save them for later. + +:::{seealso} +[](../getting_started/concepts.md#the-document-tree) introduces the elements of the tree and +[](exporting.md) covers the text-oriented export formats (Markdown, HTML, hOCR...). +::: + +## Navigate the tree + +```python +from doctr.io import DocumentFile +from doctr.models import ocr_predictor + +predictor = ocr_predictor(pretrained=True) +result = predictor(DocumentFile.from_pdf("invoice.pdf")) + +page = result.pages[0] +page.dimensions # (height, width) of the page image, in pixels: (1684, 1191) +page.page # the page image itself, as a NumPy array +page.blocks # list of Block +page.orientation # {"value": ..., "confidence": ...} with detect_orientation=True +page.language # {"value": ..., "confidence": ...} with detect_language=True +page.layout # list of LayoutElement with detect_layout=True +page.tables # list of Table with detect_tables=True +``` + +Most of the time, you want a flat list of words. A small generator does the job: + +```python +def iter_words(document): + for page_idx, page in enumerate(document.pages): + for block in page.blocks: + for line in block.lines: + for word in line.words: + yield page_idx, word + + +words = [word.value for _, word in iter_words(result)] +``` + +Every element also has a `render()` method that returns its text: `line.render()` joins the words of a line with +spaces, `page.render()` returns the whole page. + +## Filter by confidence + +`word.confidence` tells how sure the recognition model is. Use it to send doubtful words to a human, or to drop +noise: + +```python +doubtful = [(page_idx, word.value, word.confidence) for page_idx, word in iter_words(result) if word.confidence < 0.5] +``` + +:::{tip} +The way character probabilities are combined into `word.confidence` is configurable: see +[confidence scores](ocr_predictor.md#confidence-scores). +::: + +## Convert coordinates to pixels + +Geometries are relative to the page size, with `(0, 0)` at the top-left corner. Multiply them by the page +dimensions to get pixels: + +::::{tab-set} + +:::{tab-item} Straight boxes +```python +import numpy as np + +height, width = page.dimensions +(xmin, ymin), (xmax, ymax) = word.geometry + +x0, y0 = int(xmin * width), int(ymin * height) +x1, y1 = int(xmax * width), int(ymax * height) + +word_image = page.page[y0:y1, x0:x1] # crop the word from the page +``` +::: + +:::{tab-item} Rotated boxes +With `assume_straight_pages=False`, geometries are `(4, 2)` arrays of points: + +```python +import numpy as np + +height, width = page.dimensions +polygon = np.asarray(word.geometry) * np.array([width, height]) # (4, 2) pixel coordinates +``` +::: +:::: + +## Find a value next to a keyword + +Combining positions and text lets you build simple extraction rules. For instance, to read the amount written to +the right of *Total*: + +```python +def value_right_of(page, keyword): + words = [w for block in page.blocks for line in block.lines for w in line.words] + anchor = next(w for w in words if w.value.lower().startswith(keyword.lower())) + (_, ay0), (ax1, ay1) = anchor.geometry + center_y = (ay0 + ay1) / 2 + # Words on the same horizontal band, to the right of the keyword, sorted left to right + candidates = [w for w in words if w.geometry[0][1] < center_y < w.geometry[1][1] and w.geometry[0][0] > ax1] + return " ".join(w.value for w in sorted(candidates, key=lambda w: w.geometry[0][0])) + + +print(value_right_of(result.pages[0], "Total")) # due 1,250.00 EUR +``` + +For forms and invoices with many fields, a [KIE predictor](kie.md) trained on your fields is more robust than +hand-written rules. + +## Visualize the predictions + +::::{tab-set} + +:::{tab-item} Interactive window +Requires the `viz` extra. Hover a box to see the word and its confidence: + +```python +result.show() # all pages; or result.pages[0].show() +``` + +When the layout model ran, its regions are drawn as well (`display_layout=False` hides them). +::: + +:::{tab-item} Image file +{func}`~doctr.utils.visualization.visualize_page` returns a Matplotlib figure that you can save, e.g. on a +server without display: + +```python +import matplotlib + +matplotlib.use("Agg") # no display needed +from doctr.utils.visualization import visualize_page + +page = result.pages[0] +fig = visualize_page(page.export(), page.page, interactive=False, add_labels=False) +fig.savefig("predictions.png", bbox_inches="tight") +``` +::: +:::: + +```{image} ../_static/images/examples/invoice_words.png +:alt: Detected words overlaid on the invoice +:width: 520px +:align: center +``` + +## Rebuild the page from its predictions + +{meth}`~doctr.io.Page.synthesize` draws the recognized words at their position on a blank page. It is a quick +way to eyeball what was read, and where: + +```python +import matplotlib.pyplot as plt + +synthetic_pages = result.synthesize() # one image per page +plt.imshow(synthetic_pages[0]) +plt.axis("off") +plt.show() +``` + +```{image} ../_static/images/examples/invoice_synthesized.png +:alt: The invoice rebuilt from the predictions +:width: 520px +:align: center +``` + +## Save and reload + +{meth}`~doctr.io.Document.export` converts the result into a nested dictionary of plain Python types, ready for +JSON. {meth}`~doctr.io.Document.from_dict` rebuilds the `Document` from it: + +```python +import json + +from doctr.io import Document + +# Save +with open("invoice.json", "w") as f: + json.dump(result.export(), f) + +# ... later: reload +with open("invoice.json") as f: + restored = Document.from_dict(json.load(f)) + +print(restored.render()) +``` + +The page images are not part of the export. Pass them back with `Document.from_dict(data, pages=pages)` if you +need them, e.g. to call `show()`. + +:::{dropdown} What does the JSON export look like? +```json +{ + "pages": [ + { + "page_idx": 0, + "dimensions": [1684, 1191], + "orientation": {"value": null, "confidence": null}, + "language": {"value": null, "confidence": null}, + "blocks": [ + { + "geometry": [[0.0706, 0.0537], [0.3992, 0.0732]], + "objectness_score": 0.83, + "lines": [ + { + "geometry": [[0.0706, 0.0537], [0.3992, 0.0732]], + "objectness_score": 0.83, + "words": [ + { + "value": "ACME", + "confidence": 0.99995, + "geometry": [[0.0706, 0.0537], [0.1603, 0.0713]], + "objectness_score": 0.80, + "crop_orientation": {"value": 0, "confidence": null} + } + ] + } + ], + "artefacts": [] + } + ], + "layout": [], + "tables": [] + } + ] +} +``` +::: diff --git a/docs/source/user_guide/rotated_documents.md b/docs/source/user_guide/rotated_documents.md new file mode 100644 index 0000000000..abfd886469 --- /dev/null +++ b/docs/source/user_guide/rotated_documents.md @@ -0,0 +1,94 @@ +# Rotated documents + +Scans are rarely perfectly aligned: pages are skewed, photographed at an angle, fed upside down into the scanner, +or contain vertical text. docTR handles all of these cases, at a small cost in speed. This page helps you choose +the right options. + +## Which setting do I need? + +| Your documents | Recommended options | +| ------------------------------------------------------------------- | ------------------------------------------------------------------ | +| Upright pages, horizontal text (digital PDFs, clean scans) | defaults (`assume_straight_pages=True`): fastest | +| Slightly skewed or rotated pages, and you want **straight boxes** | `straighten_pages=True` | +| Pages in any orientation, text in several directions | `assume_straight_pages=False` | +| As above, but your application only understands rectangles | `assume_straight_pages=False, export_as_straight_boxes=True` | +| You only want to **know** how each page is rotated | `detect_orientation=True` | + +## Straight pages (default) + +With `assume_straight_pages=True`, docTR fits axis-aligned boxes to the text and reads the crops as they are. +This is the fastest mode and the right one for most digital documents. Geometries are +`((xmin, ymin), (xmax, ymax))` pairs. + +## Straighten the pages first + +`straighten_pages=True` estimates the rotation of each page, rotates the page back, and only then runs text +detection and recognition. The output looks as if the page had been scanned straight: straight boxes, and a +reading order that follows the text. + +```python +from doctr.models import ocr_predictor + +predictor = ocr_predictor(pretrained=True, straighten_pages=True) +``` + +The coordinates refer to the **straightened** page, which may have different dimensions than the input (check +`page.dimensions`). To get coordinates on the original page instead, add `preserve_original_coords=True`. + +## Rotated boxes + +`assume_straight_pages=False` makes the detection model predict **rotated boxes**, and enables two small +classifiers: + +- a **page orientation** classifier, which estimates the general orientation of each page; +- a **crop orientation** classifier, which rotates each word crop upright (0°, 90°, 180° or 270°) before it is read. + +```python +predictor = ocr_predictor(pretrained=True, assume_straight_pages=False) +result = predictor(pages) + +word = result.pages[0].blocks[0].lines[0].words[0] +word.geometry # four (x, y) points: ((x1, y1), (x2, y2), (x3, y3), (x4, y4)) +word.crop_orientation # {'value': 0, 'confidence': 1.0} +``` + +Add `export_as_straight_boxes=True` to get the enclosing axis-aligned boxes instead of polygons. + +## Detect the page orientation + +`detect_orientation=True` adds the estimated rotation of each page, in degrees, to the results. It works with every +other option: + +```python +predictor = ocr_predictor(pretrained=True, assume_straight_pages=False, detect_orientation=True) +result = predictor(pages) + +print(result.pages[0].orientation) # {'value': -10, 'confidence': None} for a page skewed by 10° +``` + +## Speed up the orientation models + +The orientation classifiers are light, but you can skip them when you know your documents well: + +`disable_page_orientation=True` +: Skip the page orientation classifier, e.g. for pages that are only slightly skewed (between about -45° and 45°). + Only relevant with `assume_straight_pages=False`, `straighten_pages=True` or `detect_orientation=True`. + +`disable_crop_orientation=True` +: Skip the word crop classifier when all the text is horizontal (never upside down or vertical). Only relevant + with `assume_straight_pages=False` or `straighten_pages=True`. + +```python +predictor = ocr_predictor( + pretrained=True, + assume_straight_pages=False, + disable_page_orientation=True, + disable_crop_orientation=True, +) +``` + +## Learn more + +- The [Dealing with rotated documents](https://colab.research.google.com/github/mindee/notebooks/blob/main/doctr/dealing_with_rotations.ipynb) + notebook compares all these modes on real examples. +- The orientation classifiers can be [retrained on your data](../training/custom_models.md#orientation-classifiers). diff --git a/docs/source/user_guide/standalone_predictors.md b/docs/source/user_guide/standalone_predictors.md new file mode 100644 index 0000000000..27d1ebfdaf --- /dev/null +++ b/docs/source/user_guide/standalone_predictors.md @@ -0,0 +1,96 @@ +# Task-specific predictors + +The OCR predictor chains several models. Each of them is also available on its own, wrapped in a predictor that +handles pre-processing, batching and post-processing. Use them when you only need one step: locating text, +reading pre-cropped words, classifying orientations... + +| Predictor | Input | Output per input image | +| ------------------------------------------------------ | ------------------------- | ------------------------------------------------------ | +| {func}`~doctr.models.detection.detection_predictor` | Pages | `{class_name: array of boxes + scores}` | +| {func}`~doctr.models.recognition.recognition_predictor` | Word crops | `(text, confidence)` | +| {func}`~doctr.models.layout.layout_predictor` | Pages | `{"class_names", "boxes", "scores"}` | +| {func}`~doctr.models.table_structure.table_predictor` | Table images | `{"cells", "num_rows", "num_cols"}` | +| {func}`~doctr.models.classification.page_orientation_predictor` | Pages | Orientation class, angle and probability | +| {func}`~doctr.models.classification.crop_orientation_predictor` | Word crops | Orientation class, angle and probability | + +All of them take a **list of images** (NumPy arrays of shape `(H, W, 3)`, `uint8`, RGB) and return one result per +image. As with the OCR predictor, pass `pretrained=True` to load trained weights. + +## Text detection + +```python +from doctr.io import DocumentFile +from doctr.models import detection_predictor + +predictor = detection_predictor("fast_base", pretrained=True) +pages = DocumentFile.from_pdf("invoice.pdf") +out = predictor(pages) + +boxes = out[0]["words"] # (N, 5): xmin, ymin, xmax, ymax, score (relative coordinates) +``` + +With `assume_straight_pages=False`, each box is a rotated polygon: the array has shape `(N, 5, 2)`, with the +four corners followed by the score. Multi-class detection models (see [](kie.md)) return one entry per class. + +The predictor accepts the same input options as the OCR predictor: `assume_straight_pages`, +`preserve_aspect_ratio`, `symmetric_pad` and `batch_size`. + +## Text recognition + +The recognition predictor reads **one word per image**. Crops are resized to the input size of the model (32 px +high), so they should be tightly cropped around the word: + +```python +from doctr.models import recognition_predictor + +predictor = recognition_predictor("crnn_vgg16_bn", pretrained=True) +out = predictor([word_crop]) # e.g. the crop of "ACME" from the sample invoice + +print(out) # [('ACME', 0.9996)] +``` + +:::{note} +The pretrained vocabularies have no space character: feed the predictor single words, not whole lines. To read +lines or pages, use the OCR predictor, which detects the words first. +::: + +Very wide crops are split into overlapping parts that are read separately and merged. `batch_size` (default +`128`) sets how many crops are processed at once. + +## Layout analysis & table structure + +See [](layout_and_tables.md#standalone-predictors). + +## Orientation classifiers + +Two lightweight classifiers predict the orientation of a page or a word crop among 0°, 90°, 180° and 270°: + +```python +from doctr.models import crop_orientation_predictor, page_orientation_predictor + +page_classifier = page_orientation_predictor(pretrained=True) +class_idxs, angles, probs = page_classifier([upright_page, rotated_page]) +print(angles) # [0, -90] + +crop_classifier = crop_orientation_predictor(pretrained=True) +class_idxs, angles, probs = crop_classifier([word_crop, upside_down_crop]) +print(angles) # [0, 180] +``` + +They are used by the OCR predictor for [rotated documents](rotated_documents.md), and can be +[retrained on your data](../training/custom_models.md#orientation-classifiers). + +## Accessing the models + +Every predictor exposes its model as `.model`, a regular PyTorch module: + +```python +predictor = detection_predictor("db_resnet50", pretrained=True) + +predictor.model # the DBNet module +predictor.model.cfg # its configuration (input shape, mean/std, url...) +predictor.model.postprocessor.bin_thresh # post-processing parameters +``` + +The [Using standalone predictors](https://colab.research.google.com/github/mindee/notebooks/blob/main/doctr/using_standalone_predictors.ipynb) +notebook shows these predictors in action. diff --git a/docs/source/using_doctr/custom_models_training.rst b/docs/source/using_doctr/custom_models_training.rst deleted file mode 100644 index bcb0fc0556..0000000000 --- a/docs/source/using_doctr/custom_models_training.rst +++ /dev/null @@ -1,221 +0,0 @@ -.. _custom_models_training: - -Train your own model -==================== - -If the pretrained models don't meet your specific needs, you have the option to train your own model using the docTR library. -For details on the training process and the necessary data and data format, refer to the following links: - -- `detection `_ -- `recognition `_ -- `layout `_ -- `table structure `_ - -If you’re looking for a lightweight yet efficient tool to annotate small amounts of data, especially tailored for docTR, -check out the `docTR Labeling Tool `_. -This tool makes it easy to create your own dataset for fine-tuning and optimizing your OCR models. - -Loading your custom trained model ---------------------------------- - -This section shows how you can easily load a custom trained model in docTR. - -.. code:: python3 - - import torch - from doctr.models import ocr_predictor, db_resnet50, crnn_vgg16_bn - - # Load custom detection model - det_model = db_resnet50(pretrained=False, pretrained_backbone=False) - det_model.from_pretrained('', map_location="cpu") - predictor = ocr_predictor(det_arch=det_model, reco_arch="vitstr_small", pretrained=True) - - # Load custom recognition model - reco_model = crnn_vgg16_bn(pretrained=False, pretrained_backbone=False) - reco_model.from_pretrained('', map_location="cpu") - predictor = ocr_predictor(det_arch="linknet_resnet18", reco_arch=reco_model, pretrained=True) - - # Load custom detection and recognition model - det_model = db_resnet50(pretrained=False, pretrained_backbone=False) - det_model.from_pretrained('', map_location="cpu") - reco_model = crnn_vgg16_bn(pretrained=False, pretrained_backbone=False) - reco_model.from_pretrained('', map_location="cpu") - predictor = ocr_predictor(det_arch=det_model, reco_arch=reco_model, pretrained=False) - - -Load a custom recognition model trained on another vocabulary as the default one (French): - -.. code:: python3 - - import torch - from doctr.models import ocr_predictor, crnn_vgg16_bn - from doctr.datasets import VOCABS - - reco_model = crnn_vgg16_bn(pretrained=False, pretrained_backbone=False, vocab=VOCABS["german"]) - reco_model.from_pretrained('') - - predictor = ocr_predictor(det_arch='linknet_resnet18', reco_arch=reco_model, pretrained=True) - - -Plug a custom layout analysis model (trained on another set of classes) directly into the OCR pipeline so the detected regions are attached to every page: - -.. code:: python3 - - import torch - from doctr.models import ocr_predictor, lw_detr_s - - # Custom layout model with your own class names - layout_model = lw_detr_s(pretrained=False, class_names=["heading", "paragraph", "figure", "table"]) - layout_model.from_pretrained('') - - # Pass it through `layout_arch`, exactly as for the detection / recognition models - predictor = ocr_predictor(pretrained=True, detect_layout=True, layout_arch=layout_model) - - result = predictor(doc) - # The regions (with your custom class names) are available on each page - print([(region.type, region.confidence) for region in result.pages[0].layout]) - - - -Plug a custom layout analysis model (trained on another set of classes) directly into the OCR pipeline so the detected regions are attached to every page: - -.. code:: python3 - - import torch - from doctr.models import ocr_predictor, lw_detr_s - - # Custom layout model with your own class names - layout_model = lw_detr_s(pretrained=False, class_names=["heading", "paragraph", "figure", "table"]) - layout_model.from_pretrained('') - - # Pass it through `layout_arch`, exactly as for the detection / recognition models - predictor = ocr_predictor(pretrained=True, detect_layout=True, layout_arch=layout_model) - - result = predictor(doc) - # The regions (with your custom class names) are available on each page - print([(region.type, region.confidence) for region in result.pages[0].layout]) - - -Load a custom trained KIE detection model: - -.. code:: python3 - - import torch - from doctr.models import kie_predictor, db_resnet50 - - det_model = db_resnet50(pretrained=False, pretrained_backbone=False, class_names=['total', 'date']) - det_model.from_pretrained('') - kie_predictor(det_arch=det_model, reco_arch='crnn_vgg16_bn', pretrained=True) - -Load a model with customized Preprocessor: - -.. code:: python3 - - import torch - from doctr.models.predictor import OCRPredictor - from doctr.models.detection.predictor import DetectionPredictor - from doctr.models.recognition.predictor import RecognitionPredictor - from doctr.models.preprocessor import PreProcessor - from doctr.models import db_resnet50, crnn_vgg16_bn - - det_model = db_resnet50(pretrained=False, pretrained_backbone=False) - det_model.from_pretrained('') - reco_model = crnn_vgg16_bn(pretrained=False, pretrained_backbone=False) - reco_model.from_pretrained('') - - det_predictor = DetectionPredictor( - PreProcessor( - (1024, 1024), - batch_size=1, - mean=(0.798, 0.785, 0.772), - std=(0.264, 0.2749, 0.287) - ), - det_model - ) - - reco_predictor = RecognitionPredictor( - PreProcessor( - (32, 128), - preserve_aspect_ratio=True, - batch_size=32, - mean=(0.694, 0.695, 0.693), - std=(0.299, 0.296, 0.301) - ), - reco_model - ) - - predictor = OCRPredictor(det_predictor, reco_predictor) - -Custom orientation classification models ----------------------------------------- - -If you work with rotated documents and make use of the orientation classification feature by passing one of the following arguments: - -* `assume_straight_pages=False` -* `detect_orientation=True` -* `straigten_pages=True` - -You can train your own orientation classification model using the docTR library. For details on the training process and the necessary data and data format, refer to the following link: - -- `orientation `_ - -**NOTE**: Currently we support only `mobilenet_v3_small` models for crop and page orientation classification. - -Loading your custom trained orientation classification model -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -.. code:: python3 - - import torch - from doctr.io import DocumentFile - from doctr.models import ocr_predictor, mobilenet_v3_small_page_orientation, mobilenet_v3_small_crop_orientation - from doctr.models.classification.zoo import crop_orientation_predictor, page_orientation_predictor - - custom_page_orientation_model = mobilenet_v3_small_page_orientation(pretrained=False) - custom_page_orientation_model.from_pretrained('') - custom_crop_orientation_model = mobilenet_v3_small_crop_orientation(pretrained=False) - custom_crop_orientation_model.from_pretrained('') - - predictor = ocr_predictor( - pretrained=True, - assume_straight_pages=False, - straighten_pages=True, - detect_orientation=True, - ) - - # Overwrite the default orientation models - predictor.crop_orientation_predictor = crop_orientation_predictor(custom_crop_orientation_model) - predictor.page_orientation_predictor = page_orientation_predictor(custom_page_orientation_model) - -Custom table structure recognition models ------------------------------------------ - -If you work with documents containing tables and make use of the table structure recognition feature by passing the following arguments: - -* `detect_tables=True` - -You can train your own table structure recognition model using the docTR library. For details on the training process and the necessary data and data format, refer to the following link: - -- `table structure recognition `_ - -Loading your custom trained table structure recognition model -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -.. code:: python3 - - import torch - from doctr.io import DocumentFile - from doctr.models import ocr_predictor, tablecenternet - from doctr.models.table_structure.zoo import table_predictor - - custom_table_structure_model = tablecenternet(pretrained=False) - custom_table_structure_model.from_pretrained('') - - predictor = ocr_predictor( - pretrained=True, - detect_layout=True, - detect_tables=True, - ) - - # Overwrite the default table structure model - predictor.table_predictor = table_predictor(custom_table_structure_model) diff --git a/docs/source/using_doctr/running_on_aws.rst b/docs/source/using_doctr/running_on_aws.rst deleted file mode 100644 index 8a5e1a4cc4..0000000000 --- a/docs/source/using_doctr/running_on_aws.rst +++ /dev/null @@ -1,10 +0,0 @@ -AWS Lambda -========== - -The security policy of `AWS Lambda `_ restricts writing outside the ``/tmp`` directory. - -To make docTR work on Lambda, you need to perform the following two steps: - -1. Disable the usage of the ``multiprocessing`` package by setting the ``DOCTR_MULTIPROCESSING_DISABLE`` environment variable to ``TRUE``. This step is necessary because the package uses the ``/dev/shm`` directory for shared memory. - -2. Change the caching directory used by docTR for models. By default, it is set to ``~/.cache/doctr``, which is outside the ``/tmp`` directory on AWS Lambda. You can modify this by setting the ``DOCTR_CACHE_DIR`` environment variable. diff --git a/docs/source/using_doctr/sharing_models.rst b/docs/source/using_doctr/sharing_models.rst deleted file mode 100644 index 37f73ef12b..0000000000 --- a/docs/source/using_doctr/sharing_models.rst +++ /dev/null @@ -1,127 +0,0 @@ -Share your model with the community -=================================== - -docTR's focus is on open source, and if you feel the same way, we appreciate you sharing your trained model with the community. -To make it easy for you, we have integrated an interface to the Hugging Face Hub. - -.. currentmodule:: doctr.models.factory - - -Loading from Hugging Face Hub -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -This section shows how you can easily load a pretrained model from the Hugging Face Hub. - -.. code:: python3 - - from doctr.io import DocumentFile - from doctr.models import ocr_predictor, from_hub - image = DocumentFile.from_images(['data/example.jpg']) - # Load a custom detection model from the Hugging Face Hub - det_model = from_hub('Felix92/doctr-torch-db-mobilenet-v3-large') - # Load a custom recognition model from the Hugging Face Hub - reco_model = from_hub('Felix92/doctr-torch-crnn-mobilenet-v3-large-french') - # You can easily plug these models in to the OCR predictor - predictor = ocr_predictor(det_arch=det_model, reco_arch=reco_model) - result = predictor(image) - - -Pushing to the Hugging Face Hub -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -You can also push your trained model to the Hugging Face Hub. -You need only to provide the task type (classification, detection, recognition or obj_detection), a name for your trained model (NOTE: -existing repositories will not be overwritten) and the model name itself. - -- Prerequisites: - - Hugging Face account (you can easily create one at https://huggingface.co/) - - installed Git LFS (check installation at: https://git-lfs.github.com/) in the repository - -.. code:: python3 - - from doctr.models import recognition, login_to_hub, push_to_hf_hub - login_to_hub() - my_awesome_model = recognition.crnn_mobilenet_v3_large(pretrained=True) - push_to_hf_hub(my_awesome_model, model_name='doctr-crnn-mobilenet-v3-large-french-v1', task='recognition', arch='crnn_mobilenet_v3_large') - -It is also possible to push your model directly after training. - -.. code:: bash - - python3 ~/doctr/references/recognition/train.py crnn_mobilenet_v3_large --name doctr-crnn-mobilenet-v3-large --push-to-hub - - -Pretrained community models -^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -This section is to provide some tables for pretrained community models. -Feel free to open a pull request or issue to add your model to this list. - -Naming conventions ------------------- - -We suggest using the following naming conventions for your models: - -**Classification:** ``doctr--`` - -**Detection:** ``doctr-`` - -**Recognition:** ``doctr--`` - -**Layout:** ``doctr-`` - -**Table structure:** ``doctr-`` - - -Classification --------------- - -+---------------------------------+-------------------------------------+-----------------------+ -| **Architecture** | **Repo_ID** | **Vocabulary** | -+=================================+=====================================+=======================+ -| resnet18 (dummy) | Felix92/doctr-dummy-torch-resnet18 | french | -+---------------------------------+-------------------------------------+-----------------------+ - - -Detection ---------- - -+---------------------------------+-------------------------------------------------+------------------------+ -| **Architecture** | **Repo_ID** | **Framework** | -+=================================+=================================================+========================+ -| db_resnet50 | rania-sr/doctr-Detection-model-v1-arabic | PyTorch | -+---------------------------------+-------------------------------------------------+------------------------+ - - -Recognition ------------ - -+---------------------------------+---------------------------------------------------+---------------------+------------------------+ -| **Architecture** | **Repo_ID** | **Language** | **Framework** | -+=================================+===================================================+=====================+========================+ -| crnn_vgg16_bn | tilman-rassy/doctr-crnn-vgg16-bn-fascan-v1 | french + german + § | PyTorch | -+---------------------------------+---------------------------------------------------+---------------------+------------------------+ -| parseq | Felix92/doctr-torch-parseq-multilingual-v1 | multilingual | PyTorch | -+---------------------------------+---------------------------------------------------+---------------------+------------------------+ -| parseq | rania-sr/doctr-model-v1-arabic | arabic | PyTorch | -+---------------------------------+---------------------------------------------------+---------------------+------------------------+ - - -Layout ------- - -+---------------------------------+---------------------------------------------------+------------------------+ -| **Architecture** | **Repo_ID** | **Framework** | -+=================================+===================================================+========================+ -| lw_detr_s (dummy) | Felix92/doctr-dummy-torch-lw-detr-s | PyTorch | -+---------------------------------+---------------------------------------------------+------------------------+ - - -Table structure ---------------- - -+---------------------------------+---------------------------------------------------+------------------------+ -| **Architecture** | **Repo_ID** | **Framework** | -+=================================+===================================================+========================+ -| tablecenternet (dummy) | Felix92/doctr-dummy-torch-tablecenternet | PyTorch | -+---------------------------------+---------------------------------------------------+------------------------+ diff --git a/docs/source/using_doctr/using_cli.rst b/docs/source/using_doctr/using_cli.rst deleted file mode 100644 index c1f4b37c16..0000000000 --- a/docs/source/using_doctr/using_cli.rst +++ /dev/null @@ -1,124 +0,0 @@ -Using the CLI for Optical Character Recognition -=============================================== - -The full Optical Character Recognition (OCR) task can be executed by using the Command Line Interface (CLI) implemented in docTR. This tool allows you to process both images and PDF files without writing a single line of Python code, and to export the results as JSON, plain text, Markdown, AsciiDoc, HTML or hOCR. - -Basic Usage ------------ - -To run the OCR engine on a file, use the following command structure: - -.. code-block:: bash - - doctr-cli --input_path path/to/your/document.pdf --output results.json - -Arguments ---------- - -The CLI supports a variety of arguments to fine-tune the detection, recognition and export process: - -**Mandatory Arguments:** - -* ``--input_path``: Path to one or more input images / PDF files. Every page is appended to a single document, so several files can be processed in one go. Values starting with ``http://`` or ``https://`` are interpreted as web pages and rendered as PDF (this requires ``weasyprint``, cf. :meth:`doctr.io.DocumentFile.from_url`). - -**General Options:** - -* ``--version``: Prints the installed docTR version and exits. - -**Input Options:** - -* ``--pdf_password``: Password used to unlock encrypted PDF files. *Default: None* -* ``--pdf_scale``: PDF rendering scale, where 1 corresponds to 72dpi. *Default: 2* - -**Architecture Selection:** - -* ``--det_arch``: The detection architecture to use (e.g., ``fast_base``). *Default: fast_base* -* ``--reco_arch``: The recognition architecture to use (e.g., ``crnn_vgg16_bn``). *Default: crnn_vgg16_bn* -* ``--layout_arch``: The layout architecture to use. *Default: lw_detr_s* -* ``--device``: Device the models are loaded on (``auto``, ``cpu``, ``cuda:0``, ...). On a CUDA device of compute capability 8.0 or above, the models are cast to bfloat16. *Default: auto* - -**Processing Options:** - -* ``--assume_straight_pages``, ``--no-assume_straight_pages``: Determine whether pages should be handled as straight or skewed pages. *Default: True* -* ``--straighten_pages``: If flagged, the tool will attempt to straighten skewed pages before analysis. *Default: False* -* ``--preserve_aspect_ratio``, ``--no-preserve_aspect_ratio``: Ensures that the aspect ratio is maintained during resizing. *Default: True* -* ``--symmetric_pad``, ``--no-symmetric_pad``: Applies symmetric padding to the input images. *Default: True* -* ``--det_bs``: Batch size used for the detection model. *Default: 2* -* ``--reco_bs``: Batch size used for the recognition model. *Default: 128* -* ``--bin_thresh``: Binarization threshold of the detection post-processing. *Default: the value of the architecture* -* ``--box_thresh``: Minimum confidence of a detected box. *Default: the value of the architecture* -* ``--confidence_aggregation``: Aggregation method of the character probabilities into the word confidence, one of ``mean``, ``min``, ``max``, ``median``, ``geometric_mean``, ``harmonic_mean``. *Default: the method of the recognition architecture* -* ``--detect_orientation``: Enables automatic detection of page orientation. *Default: False* -* ``--detect_language``: Enables language detection for the extracted text. *Default: False* -* ``--detect_layout``: Attaches the detected layout regions to each page. *Default: False* -* ``--detect_tables``: Regroups the words of the detected tables into structured tables. This enables the layout model. *Default: False* -* ``--ignore_regions``: Layout class names to mask out before detection & recognition (e.g. ``--ignore_regions Picture Table``). This enables the layout model. *Default: None* -* ``--export_as_straight_boxes``: Exports (potentially rotated) predictions as straight boxes. *Default: False* -* ``--preserve_original_coords``: Maps the boxes back to the original page coordinates when ``--straighten_pages`` is used. *Default: False* -* ``--disable_page_orientation``: Disables the page orientation classifier. *Default: False* -* ``--disable_crop_orientation``: Disables the crop orientation classifier. *Default: False* - -**Document Assembling Options:** - -* ``--resolve_lines``, ``--no-resolve_lines``: Groups the words into lines. *Default: True* -* ``--resolve_blocks``: Groups the lines into blocks. *Default: False* -* ``--paragraph_break``: Relative length of the minimum space separating two paragraphs. *Default: 0.035* -* ``--keep_reading_order``: Arranges the content of every page in reading order. *Default: False* - -**Output Options:** - -* ``--output``: The destination path where the results will be saved. *Default: results.json* -* ``--format``: The export format, one of ``json``/``dict``, ``txt``/``text``, ``md``/``markdown``, ``adoc``/``asciidoc``, ``html``, ``xml``/``hocr``. When it is not set, the format is inferred from the extension of ``--output``, and falls back to JSON. *Default: None* -* ``--direction``: Reading direction of the document, one of ``auto``, ``ltr``, ``rtl``, ``ttb-rtl``, ``ttb-ltr``. *Default: auto* -* ``--reading_order``, ``--no-reading_order``: Linearizes the content in reading order (JSON & hOCR exports). *Default: True* -* ``--escape``, ``--no-escape``: Escapes the characters carrying a structural meaning (Markdown & AsciiDoc exports). *Default: True* -* ``--include_furniture``, ``--no-include_furniture``: Includes page headers, page footers and footnotes (text-like exports). *Default: True* -* ``--file_title``: Title of the exported hOCR files. *Default: docTR - XML export (hOCR)* -* ``--indent``: Indentation of the JSON export. *Default: 4* -* ``--quiet``: Only logs errors. *Default: False* - -Examples --------- - -**Running OCR on an image:** - -.. code-block:: bash - - doctr-cli --input_path image.jpg --output ocr_res.json - -**Running OCR on several files at once:** - -.. code-block:: bash - - doctr-cli --input_path page1.jpg page2.jpg scan.pdf --output ocr_res.json - -**Using a specific detection architecture and straightening pages:** - -.. code-block:: bash - - doctr-cli --input_path doc.pdf --det_arch db_mobilenet_v3_large --straighten_pages - -**Exporting the text as Markdown, with tables and layout:** - -.. code-block:: bash - - doctr-cli --input_path doc.pdf --detect_tables --output doc.md - -**Exporting as hOCR on the GPU, ignoring the pictures of the document:** - -.. code-block:: bash - - doctr-cli --input_path doc.pdf --device cuda:0 --ignore_regions Picture --output doc.xml - -Output Format -------------- - -By default, the results are exported in a structured JSON format containing: - -* **Pages**: Dimensions, orientation, language and layout regions. -* **Blocks**: Grouping of lines. -* **Lines**: Grouping of words. -* **Words**: The actual text content with confidence scores and bounding box coordinates. -* **Tables**: The structured tables, when ``--detect_tables`` is used. - -The text-like formats (``txt``, ``md``, ``adoc``, ``html``) are written to a single file. Since hOCR describes a single page per file, the ``xml`` export writes one file per page: an ``--output`` of ``doc.xml`` on a 3-page document produces ``doc_page_1.xml``, ``doc_page_2.xml`` and ``doc_page_3.xml``. diff --git a/docs/source/using_doctr/using_contrib_modules.rst b/docs/source/using_doctr/using_contrib_modules.rst deleted file mode 100644 index 30b86b0846..0000000000 --- a/docs/source/using_doctr/using_contrib_modules.rst +++ /dev/null @@ -1,51 +0,0 @@ -Integrate contributions into your pipeline -========================================== - -The `contrib` module provides a collection of additional features which could be relevant for your document analysis pipeline. -The following sections will give you an overview of the available modules and features. - -.. currentmodule:: doctr.contrib - - -Available contribution modules ------------------------------- - -**NOTE:** To use the contrib module, you need to install the `onnxruntime` package. You can install it using the following command: - -.. code:: bash - - pip install python-doctr[contrib] - # Or - pip install onnxruntime # pip install onnxruntime-gpu - -Here are all contribution modules that are available through docTR: - -ArtefactDetector -^^^^^^^^^^^^^^^^^ - -The ArtefactDetector module provides a set of functions to detect artefacts in the document images, such as logos, QR codes, bar codes, etc. -It is based on the YOLOv8 architecture, which is a state-of-the-art object detection model. - -.. code:: python3 - - from doctr.io import DocumentFile - from doctr.contrib.artefacts import ArtefactDetector - - # Load the document - doc = DocumentFile.from_images(["path/to/your/image"]) - detector = ArtefactDetector(batch_size=2, conf_threshold=0.5, iou_threshold=0.5) - artefacts = detector(doc) - - # Visualize the detected artefacts - detector.show() - -You can also use your custom trained YOLOv8 model to detect artefacts or anything else you need. -Reference: `YOLOv8 `_ - -**NOTE:** The YOLOv8 model (no Oriented Bounding Box (OBB) inference supported yet) needs to be provided as onnx exported model with a dynamic batch size. - -.. code:: python3 - - from doctr.contrib import ArtefactDetector - - detector = ArtefactDetector(model_path="path/to/your/model.onnx", labels=["table", "figure"]) diff --git a/docs/source/using_doctr/using_datasets.rst b/docs/source/using_doctr/using_datasets.rst deleted file mode 100644 index 8a9f1a0259..0000000000 --- a/docs/source/using_doctr/using_datasets.rst +++ /dev/null @@ -1,188 +0,0 @@ -Choose a ready to use dataset -============================= - -Whether it is for training or for evaluation, having predefined objects to access datasets in your prefered framework -can be a significant save of time. - -.. currentmodule:: doctr.datasets - - -Available Datasets ------------------- -In the package reference you will also find some samples for each dataset. - -Here are all datasets that are available through docTR: - -Detection -^^^^^^^^^ - -This datasets contains the information to train or validate a text detection model. - -+-----------------------------+---------------------------------+---------------------------------+---------------------------------------+ -| **Dataset** | **Train Samples** | **Test Samples** | **Information** | -+=============================+=================================+=================================+=======================================+ -| FUNSD | 149 | 50 | | -+-----------------------------+---------------------------------+---------------------------------+---------------------------------------+ -| SROIE | 626 | 360 | | -+-----------------------------+---------------------------------+---------------------------------+---------------------------------------+ -| CORD | 800 | 100 | | -+-----------------------------+---------------------------------+---------------------------------+---------------------------------------+ -| IIIT5K | 2000 | 3000 | | -+-----------------------------+---------------------------------+---------------------------------+---------------------------------------+ -| SVT | 100 | 249 | | -+-----------------------------+---------------------------------+---------------------------------+---------------------------------------+ -| SVHN | 33402 | 13068 | Character Localization | -+-----------------------------+---------------------------------+---------------------------------+---------------------------------------+ -| SynthText | 772875 | 85875 | | -+-----------------------------+---------------------------------+---------------------------------+---------------------------------------+ -| IC03 | 246 | 249 | | -+-----------------------------+---------------------------------+---------------------------------+---------------------------------------+ -| IC13 | 229 | 233 | external resources | -+-----------------------------+---------------------------------+---------------------------------+---------------------------------------+ -| IMGUR5K | 7149 | 796 | Handwritten / external resources | -+-----------------------------+---------------------------------+---------------------------------+---------------------------------------+ -| WILDRECEIPT | 1268 | 472 | external resources | -+-----------------------------+---------------------------------+---------------------------------+---------------------------------------+ -| COCOTEXT | 13880 | 3261 | external resources / legible filtered | -+-----------------------------+---------------------------------+---------------------------------+---------------------------------------+ - -.. code:: python3 - - from doctr.datasets import CORD - # Load straight boxes - train_set = CORD(train=True, download=True, detection_task=True) - # Load rotated boxes - train_set = CORD(train=True, download=True, use_polygons=True, detection_task=True) - img, target = train_set[0] - - -Recognition -^^^^^^^^^^^ - -This datasets contains the information to train or validate a text recognition model. - -+-----------------------------+---------------------------------+---------------------------------+--------------------------------------------------+ -| **Dataset** | **Train Samples** | **Test Samples** | **Information** | -+=============================+=================================+=================================+==================================================+ -| FUNSD | 21888 | 8707 | english | -+-----------------------------+---------------------------------+---------------------------------+--------------------------------------------------+ -| SROIE | 33608 | 19342 | english / only uppercase labels | -+-----------------------------+---------------------------------+---------------------------------+--------------------------------------------------+ -| CORD | 19370 | 2186 | english | -+-----------------------------+---------------------------------+---------------------------------+--------------------------------------------------+ -| IIIT5K | 2000 | 3000 | english | -+-----------------------------+---------------------------------+---------------------------------+--------------------------------------------------+ -| SVT | 257 | 647 | english / only uppercase labels | -+-----------------------------+---------------------------------+---------------------------------+--------------------------------------------------+ -| SVHN | 73257 | 26032 | digits | -+-----------------------------+---------------------------------+---------------------------------+--------------------------------------------------+ -| SynthText | ~7100000 | 707470 | english | -+-----------------------------+---------------------------------+---------------------------------+--------------------------------------------------+ -| IC03 | 1156 | 1107 | english | -+-----------------------------+---------------------------------+---------------------------------+--------------------------------------------------+ -| IC13 | 849 | 1095 | english / external resources | -+-----------------------------+---------------------------------+---------------------------------+--------------------------------------------------+ -| IMGUR5K | 207901 | 22672 | english / handwritten / external resources | -+-----------------------------+---------------------------------+---------------------------------+--------------------------------------------------+ -| MJSynth | 7581382 | 1337891 | english / external resources | -+-----------------------------+---------------------------------+---------------------------------+--------------------------------------------------+ -| IIITHWS | 7141797 | 793533 | english / handwritten / external resources | -+-----------------------------+---------------------------------+---------------------------------+--------------------------------------------------+ -| WILDRECEIPT | 49377 | 19598 | english / external resources | -+-----------------------------+---------------------------------+---------------------------------+--------------------------------------------------+ -| COCOTEXT | 59130 | 13258 | english / external resources / legible filtered | -+-----------------------------+---------------------------------+---------------------------------+--------------------------------------------------+ - -.. code:: python3 - - from doctr.datasets import CORD - # Crop boxes as is (can contain irregular) - train_set = CORD(train=True, download=True, recognition_task=True) - # Crop rotated boxes (always regular) - train_set = CORD(train=True, download=True, use_polygons=True, recognition_task=True) - img, target = train_set[0] - - -OCR -^^^ - -The same dataset table as for detection, but with information about the bounding boxes and labels. - -.. code:: python3 - - from doctr.datasets import CORD - # Load straight boxes - train_set = CORD(train=True, download=True) - # Load rotated boxes - train_set = CORD(train=True, download=True, use_polygons=True) - img, target = train_set[0] - - -Object Detection -^^^^^^^^^^^^^^^^ - -This datasets contains the information to train or validate a object detection model. - -+-----------------------------+---------------------------------+---------------------------------+-------------------------------------------------------+ -| **Dataset** | **Train Samples** | **Test Samples** | **Information** | -+=============================+=================================+=================================+=======================================================+ -| DocArtefacts | 2700 | 300 |["background", "qr_code", "bar_code", "logo", "photo"] | -+-----------------------------+---------------------------------+---------------------------------+-------------------------------------------------------+ - -.. code:: python3 - - from doctr.datasets import DocArtefacts - train_set = DocArtefacts(train=True, download=True) - img, target = train_set[0] - - -Synthetic dataset generator -^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -docTR provides also some generator objects, which can be used to generate synthetic datasets. -Both are also integrated in the training scripts to train a classification or recognition model. - -.. code:: python3 - - from doctr.datasets import CharacterGenerator - ds = CharacterGenerator(vocab='abdef', num_samples=100) - img, target = ds[0] - -.. code:: python3 - - from doctr.datasets import WordGenerator - ds = WordGenerator(vocab='abdef', min_chars=1, max_chars=32, num_samples=100) - img, target = ds[0] - - -Use your own datasets ---------------------- - -Since many documents include sensitive / personal information, we are not able to share all the data that has been used for this project. -However, we provide some guidance on how to format your own dataset into the same format so that you can use all docTR tools more easily. -You can find further information about the format in references. - -.. code:: python3 - - from doctr.datasets import DetectionDataset - # Load a detection dataset - train_set = DetectionDataset(img_folder="/path/to/images", label_path="/path/to/labels.json") - # Load a recognition Dataset - train_set = RecognitionDataset(img_folder="/path/to/images", labels_path="/path/to/labels.json") - # Load a OCR dataset which contains annotations for the boxes and labels - train_set = OCRDataset(img_folder="/path/to/images", label_file="/path/to/labels.json") - img, target = train_set[0] - - -Data Loading ------------- - -Each dataset has its specific way to load a sample, but handling batch aggregation and the underlying iterator is a task deferred to another object in docTR. - -.. code:: python3 - - from doctr.datasets import CORD, DataLoader - train_set = CORD(train=True, download=True) - train_loader = DataLoader(train_set, batch_size=32) - train_iter = iter(train_loader) - images, targets = next(train_iter) diff --git a/docs/source/using_doctr/using_model_export.rst b/docs/source/using_doctr/using_model_export.rst deleted file mode 100644 index 4aafce335f..0000000000 --- a/docs/source/using_doctr/using_model_export.rst +++ /dev/null @@ -1,151 +0,0 @@ -Preparing your model for inference -================================== - -A well-trained model is a good achievement but you might want to tune a few things to make it production-ready! - -.. currentmodule:: doctr.models.utils - - -Model optimization ------------------- - -This section is meant to help you perform inference with optimized versions of your model. - -.. _half-precision: - - -Half-precision -^^^^^^^^^^^^^^ - -**NOTE:** We support half-precision inference for PyTorch models only on **GPU devices**. - -Half-precision formats occupy 16 bits in computer memory instead of the 32 bits used by -single-precision (FP32). Two formats are supported: - -- **BF16** (``bfloat16``): keeps the same exponent range as FP32 with a reduced mantissa. -- **FP16** (``float16``): higher precision than BF16, but a much narrower dynamic range. - -Advantages: - -- Faster inference -- Less memory usage - -We recommend **BF16 over FP16**. Because it retains the full FP32 exponent range, BF16 is far -less prone to overflow and underflow. BF16 requires an Ampere-generation GPU or newer -(compute capability 8.0+); on older hardware, use FP16 instead. - -.. code:: python3 - - import torch - predictor = ocr_predictor( - reco_arch="crnn_mobilenet_v3_small", - det_arch="linknet_resnet34", - pretrained=True - ).cuda().bfloat16() # or .half() for FP16 - res = predictor(doc) - - -Compiling your models (PyTorch only) -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -**NOTE:** - -- This feature is only available if you use PyTorch as backend. -- The recognition architecture `master` is not supported for model compilation yet. -- We provide only official support for the default (`inductor`) backend, but you can try other backends, configurations depending on your hardware and requirements as well. - -Compiling your PyTorch models with `torch.compile` optimizes the model by converting it to a graph representation and applying backends that can improve performance. -This process can make inference faster and reduce memory overhead during execution. - -Further information can be found in the `PyTorch documentation `_. - -.. code:: - - import torch - from doctr.models import ( - ocr_predictor, - vitstr_small, - fast_base, - mobilenet_v3_small_crop_orientation, - mobilenet_v3_small_page_orientation, - crop_orientation_predictor, - page_orientation_predictor - ) - - # Compile the models - detection_model = torch.compile( - fast_base(pretrained=True).eval() - ) - recognition_model = torch.compile( - vitstr_small(pretrained=True).eval() - ) - crop_orientation_model = torch.compile( - mobilenet_v3_small_crop_orientation(pretrained=True).eval() - ) - page_orientation_model = torch.compile( - mobilenet_v3_small_page_orientation(pretrained=True).eval() - ) - - predictor = ocr_predictor( - detection_model, recognition_model, assume_straight_pages=False - ) - # NOTE: Only required for non-straight pages (`assume_straight_pages=False`) and non-disabled orientation classification - # Set the orientation predictors - predictor.crop_orientation_predictor = crop_orientation_predictor(crop_orientation_model) - predictor.page_orientation_predictor = page_orientation_predictor(page_orientation_model) - - compiled_out = predictor(doc) - -Export to ONNX -^^^^^^^^^^^^^^ - -ONNX (Open Neural Network Exchange) is an open and interoperable format for representing and exchanging machine learning models. -It defines a common format for representing models, including the network structure, layer types, parameters, and metadata. - -.. code:: python3 - - import torch - from doctr.models import vitstr_small - from doctr.models.utils import export_model_to_onnx - - batch_size = 1 - input_shape = (3, 32, 128) - model = vitstr_small(pretrained=True, exportable=True) - dummy_input = torch.rand((batch_size, *input_shape), dtype=torch.float32) - model_path = export_model_to_onnx( - model, - model_name="vitstr.onnx", - dummy_input=dummy_input - ) - - -Using your ONNX exported model -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -To use your exported model, we have build a dedicated lightweight package called `OnnxTR `_. -The package doesn't require PyTorch or TensorFlow to be installed - build on top of ONNXRuntime. -It is simple and easy-to-use (with the same interface you know already from docTR), that allows you to perform inference with your exported model. - -- `Installation `_ -- `Loading custom exported model `_ - -.. code:: shell - - pip install onnxtr[cpu] - -.. code:: python3 - - from onnxtr.io import DocumentFile - from onnxtr.models import ocr_predictor, parseq, linknet_resnet18 - # Load your documents - single_img_doc = DocumentFile.from_images("path/to/your/img.jpg") - - # Load your exported model/s - reco_model = parseq("path_to_custom_model.onnx", vocab="ABC") - det_model = linknet_resnet18("path_to_custom_model.onnx") - predictor = ocr_predictor(det_arch=det_model, reco_arch=reco_model) - # Or use any of the pre-trained models - predictor = ocr_predictor(det_arch="linknet_resnet18", reco_arch="parseq") - - # Get your results - res = predictor(single_img_doc) diff --git a/docs/source/using_doctr/using_models.rst b/docs/source/using_doctr/using_models.rst deleted file mode 100644 index af4012d076..0000000000 --- a/docs/source/using_doctr/using_models.rst +++ /dev/null @@ -1,836 +0,0 @@ -.. _using_models: - -Choosing the right model -======================== - -The full Optical Character Recognition task can be seen as two consecutive tasks: text detection and text recognition. -Either performed at once or separately, to each task corresponds a type of deep learning architecture. - -For a given task, docTR provides a Predictor, which is composed of 2 components: - -* PreProcessor: a module in charge of making inputs directly usable by the deep learning model. -* Model: a deep learning model, implemented with all supported deep learning backends (PyTorch) along with its specific post-processor to make outputs structured and reusable. - - -Which predictor should I use? ------------------------------- - -.. list-table:: - :widths: 60 40 - :header-rows: 1 - - * - I want to… - - Use - * - Extract all text (words, lines, layout hierarchy) from a document - - :py:meth:`ocr_predictor ` - * - Detect document regions by type (tables, figures, headers, …) - - :py:meth:`layout_predictor ` - * - Get word bounding-boxes only, without recognition - - :py:meth:`detection_predictor ` - * - Transcribe pre-cropped word images to strings - - :py:meth:`recognition_predictor ` - * - Detect the structure of a table (cell bounding-boxes and logical coordinates) - - :py:meth:`table_predictor ` - -For :doc:`custom model loading ` or sharing models, see the dedicated pages. - - -Text Detection --------------- - -The task consists of localizing textual elements in a given image. -While those text elements can represent many things, in docTR, we will consider uninterrupted character sequences (words). Additionally, the localization can take several forms: from straight bounding boxes (delimited by the 2D coordinates of the top-left and bottom-right corner), to polygons, or binary segmentation (flagging which pixels belong to this element, and which don't). -Our latest detection models works with rotated and skewed documents! - -Available detection architectures -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The following architectures are currently supported: - -* :py:meth:`linknet_resnet34 ` -* :py:meth:`linknet_resnet50 ` -* :py:meth:`db_resnet50 ` -* :py:meth:`db_mobilenet_v3_large ` -* :py:meth:`fast_tiny ` -* :py:meth:`fast_small ` -* :py:meth:`fast_base ` - -For a comprehensive comparison, we have compiled a detailed benchmark on publicly available datasets: - - -+------------------------------------------------------------------------------------+----------------------------+----------------------------+--------------------+ -| | FUNSD | CORD | | -+==================================================+=================+===============+============+===============+============+===============+====================+ -| **Architecture** | **Input shape** | **# params** | **Recall** | **Precision** | **Recall** | **Precision** | **sec/it (B: 1)** | -+--------------------------------------------------+-----------------+---------------+------------+---------------+------------+---------------+--------------------+ -| db_resnet34 | (1024, 1024, 3) | 22.4 M | 82.76 | 76.75 | 89.20 | 71.74 | 0.8 | -+--------------------------------------------------+-----------------+---------------+------------+---------------+------------+---------------+--------------------+ -| db_resnet50 | (1024, 1024, 3) | 25.4 M | 83.56 | 86.68 | 92.61 | 86.39 | 1.1 | -+--------------------------------------------------+-----------------+---------------+------------+---------------+------------+---------------+--------------------+ -| db_mobilenet_v3_large | (1024, 1024, 3) | 4.2 M | 82.69 | 84.63 | 94.51 | 70.28 | 0.5 | -+--------------------------------------------------+-----------------+---------------+------------+---------------+------------+---------------+--------------------+ -| linknet_resnet18 | (1024, 1024, 3) | 11.5 M | 81.64 | 85.52 | 88.92 | 82.74 | 0.6 | -+--------------------------------------------------+-----------------+---------------+------------+---------------+------------+---------------+--------------------+ -| linknet_resnet34 | (1024, 1024, 3) | 21.6 M | 81.62 | 82.95 | 86.26 | 81.06 | 0.7 | -+--------------------------------------------------+-----------------+---------------+------------+---------------+------------+---------------+--------------------+ -| linknet_resnet50 | (1024, 1024, 3) | 28.8 M | 81.78 | 82.47 | 87.29 | 85.54 | 1.0 | -+--------------------------------------------------+-----------------+---------------+------------+---------------+------------+---------------+--------------------+ -| fast_tiny | (1024, 1024, 3) | 13.5 M (8.5M) | 84.90 | 85.04 | 93.73 | 76.26 | 0.7 (0.4) | -+--------------------------------------------------+-----------------+---------------+------------+---------------+------------+---------------+--------------------+ -| fast_small | (1024, 1024, 3) | 14.7 M (9.7M) | 85.36 | 86.68 | 94.09 | 78.53 | 0.7 (0.5) | -+--------------------------------------------------+-----------------+---------------+------------+---------------+------------+---------------+--------------------+ -| fast_base | (1024, 1024, 3) | 16.3 M (10.6M)| 84.95 | 86.73 | 94.39 | 85.36 | 0.8 (0.5) | -+--------------------------------------------------+-----------------+---------------+------------+---------------+------------+---------------+--------------------+ - - -All text detection models above have been evaluated using both the training and evaluation sets of FUNSD and CORD (cf. :ref:`datasets`). -Explanations about the metrics being used are available in :ref:`metrics`. - -*Disclaimer: both FUNSD subsets combined have 199 pages which might not be representative enough of the model capabilities* - -Seconds per iteration (with a batch size of 1) is computed after a warmup phase of 100 tensors, by measuring the average number of processed tensors per second over 1000 samples. Those results were obtained on a `11th Gen Intel(R) Core(TM) i7-11800H @ 2.30GHz`. - - -Detection predictors -^^^^^^^^^^^^^^^^^^^^ - -:py:meth:`detection_predictor ` wraps your detection model to make it easily usable with your favorite deep learning framework seamlessly. - -.. code:: python3 - - import numpy as np - from doctr.models import detection_predictor - model = detection_predictor('db_resnet50') - dummy_img = (255 * np.random.rand(800, 600, 3)).astype(np.uint8) - out = model([dummy_img]) - -You can pass specific boolean arguments to the predictor: - -* ``pretrained``: if you want to use a model that has been pretrained on a specific dataset, setting ``pretrained=True`` will load the corresponding weights. If ``pretrained=False`` (the default), the model is randomly initialized and will produce no useful results. -* ``assume_straight_pages``: if you work with straight documents only, it will fit straight bounding boxes to the text areas. -* ``preserve_aspect_ratio``: if you want to preserve the aspect ratio of your documents while resizing before sending them to the model. -* ``symmetric_pad``: if you choose to preserve the aspect ratio, it will pad the image symmetrically and not from the bottom-right. - -.. code:: python3 - - from doctr.models import detection_predictor - predictor = detection_predictor('db_resnet50', pretrained=True, assume_straight_pages=False, preserve_aspect_ratio=True) - - -Text Recognition ----------------- - -The task consists of transcribing the character sequence in a given image. - - -Available architectures -^^^^^^^^^^^^^^^^^^^^^^^ - -The following architectures are currently supported: - -* :py:meth:`crnn_vgg16_bn ` -* :py:meth:`crnn_mobilenet_v3_small ` -* :py:meth:`crnn_mobilenet_v3_large ` -* :py:meth:`sar_resnet31 ` -* :py:meth:`master ` -* :py:meth:`vitstr_small ` -* :py:meth:`vitstr_base ` -* :py:meth:`parseq ` -* :py:meth:`viptr_tiny ` - - -For a comprehensive comparison, we have compiled a detailed benchmark on publicly available datasets: - - -+-----------------------------------------------------------------------------------+----------------------------+----------------------------+--------------------+ -| | FUNSD | CORD | | -+==================================================+=================+==============+============+===============+============+===============+====================+ -| **Architecture** | **Input shape** | **# params** | **Exact** | **Partial** | **Exact** | **Partial** | **sec/it (B: 64)** | -+--------------------------------------------------+-----------------+--------------+------------+---------------+------------+---------------+--------------------+ -| crnn_vgg16_bn | (32, 128, 3) | 15.8 M | 88.21 | 88.95 | 95.47 | 95.91 | 0.6 | -+--------------------------------------------------+-----------------+--------------+------------+---------------+------------+---------------+--------------------+ -| crnn_mobilenet_v3_small | (32, 128, 3) | 2.1 M | 87.25 | 87.99 | 93.91 | 94.34 | 0.05 | -+--------------------------------------------------+-----------------+--------------+------------+---------------+------------+---------------+--------------------+ -| crnn_mobilenet_v3_large | (32, 128, 3) | 4.5 M | 87.38 | 88.09 | 94.46 | 94.92 | 0.08 | -+--------------------------------------------------+-----------------+--------------+------------+---------------+------------+---------------+--------------------+ -| master | (32, 128, 3) | 58.7 M | 88.57 | 89.39 | 95.73 | 96.21 | 17.6 | -+--------------------------------------------------+-----------------+--------------+------------+---------------+------------+---------------+--------------------+ -| sar_resnet31 | (32, 128, 3) | 55.4 M | 88.10 | 88.88 | 94.83 | 95.29 | 4.9 | -+--------------------------------------------------+-----------------+--------------+------------+---------------+------------+---------------+--------------------+ -| vitstr_small | (32, 128, 3) | 21.4 M | 88.00 | 88.82 | 95.40 | 95.78 | 1.5 | -+--------------------------------------------------+-----------------+--------------+------------+---------------+------------+---------------+--------------------+ -| vitstr_base | (32, 128, 3) | 85.2 M | 88.33 | 89.09 | 95.32 | 95.71 | 4.1 | -+--------------------------------------------------+-----------------+--------------+------------+---------------+------------+---------------+--------------------+ -| parseq | (32, 128, 3) | 23.8 M | 88.53 | 89.24 | 95.56 | 95.91 | 2.2 | -+--------------------------------------------------+-----------------+--------------+------------+---------------+------------+---------------+--------------------+ -| viptr_tiny | (32, 128, 3) | 3.2 M | 86.03 | 86.71 | 93.08 | 93.47 | 0.08 | -+--------------------------------------------------+-----------------+--------------+------------+---------------+------------+---------------+--------------------+ - - -All text recognition models above have been evaluated using both the training and evaluation sets of FUNSD and CORD (cf. :ref:`datasets`). -Explanations about the metric being used (exact match) are available in :ref:`metrics`. - -While most of our recognition models were trained on our french vocab (cf. :ref:`vocabs`), you can easily access the vocab of any model as follows: - -.. code:: python3 - - from doctr.models import recognition_predictor - predictor = recognition_predictor('crnn_vgg16_bn') - print(predictor.model.cfg['vocab']) - - -*Disclaimer: both FUNSD subsets combined have 30595 word-level crops which might not be representative enough of the model capabilities* - -Seconds per iteration (with a batch size of 64) is computed after a warmup phase of 100 tensors, by measuring the average number of processed tensors per second over 1000 samples. Those results were obtained on a `11th Gen Intel(R) Core(TM) i7-11800H @ 2.30GHz`. - - -Recognition predictors -^^^^^^^^^^^^^^^^^^^^^^ -:py:meth:`recognition_predictor ` wraps your recognition model to make it easily usable with your favorite deep learning framework seamlessly. - -.. code:: python3 - - import numpy as np - from doctr.models import recognition_predictor - model = recognition_predictor('crnn_vgg16_bn') - dummy_img = (255 * np.random.rand(50, 150, 3)).astype(np.uint8) - out = model([dummy_img]) - - -Layout Analysis ---------------- - -The task consists of localizing and classifying visual elements in a given image. -This is a more general task than text detection, as it can be used to detect and classify any type of visual element in a document, such as tables, figures, headers, footers, etc. -Our latest layout models works with rotated and skewed documents! - -Available layout architectures -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The following architectures are currently supported: - -* :py:meth:`lw_detr_s ` -* :py:meth:`lw_detr_m ` - -For a comprehensive comparison, we have compiled a detailed benchmark: - -+--------------------------------------------------+-----------------+---------------+------------------+-------------+--------------+--------------------+ -| | | | | | | | -+==================================================+=================+===============+==================+=============+==============+====================+ -| **Architecture** | **Input shape** | **# params** | **mAP@[.5:.95]** | **AP@[.5]** | **AP@[.75]** | **sec/it (B: 1)** | -+--------------------------------------------------+-----------------+---------------+------------------+-------------+--------------+--------------------+ -| lw_detr_s | (1024, 1024, 3) | 15.1 M | 66.89 | 82.83 | 73.75 | 0.5 | -+--------------------------------------------------+-----------------+---------------+------------------+-------------+--------------+--------------------+ -| lw_detr_m | (1024, 1024, 3) | 29.5 M | soon | soon | soon | 0.7 | -+--------------------------------------------------+-----------------+---------------+------------------+-------------+--------------+--------------------+ - - -Explanations about the metrics being used are available in :ref:`metrics`. - -Seconds per iteration (with a batch size of 1) is computed after a warmup phase of 100 tensors, by measuring the average number of processed tensors per second over 1000 samples. Those results were obtained on a `11th Gen Intel(R) Core(TM) i7-11800H @ 2.30GHz`. - - -Layout predictors -^^^^^^^^^^^^^^^^^ - -:py:meth:`layout_predictor ` wraps your layout model to make it easily usable with your favorite deep learning framework seamlessly. - -.. code:: python3 - - import numpy as np - from doctr.models import layout_predictor - model = layout_predictor('lw_detr_s') - dummy_img = (255 * np.random.rand(800, 600, 3)).astype(np.uint8) - out = model([dummy_img]) - -You can pass specific boolean arguments to the predictor: - -* ``pretrained``: if you want to use a model that has been pretrained on a specific dataset, setting ``pretrained=True`` will load the corresponding weights. If ``pretrained=False`` (the default), the model is randomly initialized and will produce no useful results. -* ``assume_straight_pages``: if you work with straight documents only, it will fit straight bounding boxes to the text areas. -* ``preserve_aspect_ratio``: if you want to preserve the aspect ratio of your documents while resizing before sending them to the model. -* ``symmetric_pad``: if you choose to preserve the aspect ratio, it will pad the image symmetrically and not from the bottom-right. - -For instance, this snippet instantiates a layout predictor able to detect text on rotated documents while preserving the aspect ratio: - -.. code:: python3 - - from doctr.models import layout_predictor - predictor = layout_predictor('lw_detr_s', pretrained=True, assume_straight_pages=False, preserve_aspect_ratio=True) - - -Table Structure Recognition ---------------------------- - -The task consists of parsing the structure of a table into a machine-understandable representation: localizing every -cell (its spatial structure) and recovering the row and column it spans (its logical structure). - -Available table architectures -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The following architectures are currently supported: - -* :py:meth:`tablecenternet ` - -For a comprehensive comparison, we have compiled a detailed benchmark on a publicly available dataset: - -+--------------------------------------------------+-----------------+---------------+--------------+---------------+------------+-------------------+--------------------+ -| **Architecture** | **Input shape** | **# params** | **Recall** | **Precision** | **F1** | **Structure acc** | **sec/it (B: 1)** | -+==================================================+=================+===============+==============+===============+============+===================+====================+ -| tablecenternet | (1024, 1024, 3) | 7.1 M | 82.31 | 96.01 | 88.64 | 77.53 | 0.5 | -+--------------------------------------------------+-----------------+---------------+--------------+---------------+------------+-------------------+--------------------+ - -.. note:: - - The reported metrics are produced by ``references/table/evaluate.py`` using the - :py:class:`TableCellMetric `: cell-detection **Recall**, **Precision** and - **F1** (cells matched above an IoU threshold of 0.5), and **Structure acc**, the share of matched cells whose - logical (row/column) coordinates are correctly predicted. - -Table structure predictors -^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:py:meth:`table_predictor ` wraps your table model so it can be used directly on -document images. For each page it returns the list of detected cells, each with its geometry, its confidence score and its logical coordinates, together with the inferred number of rows -and columns. - -.. code:: python3 - - import numpy as np - from doctr.models import table_predictor - model = table_predictor('tablecenternet', pretrained=True) - table_crop = (255 * np.random.rand(800, 600, 3)).astype(np.uint8) - out = model([table_crop]) - # out[0] -> {"cells": [{"geometry": ..., "score": ..., "row_start": 0, "row_end": 0, - # "col_start": 0, "col_end": 0}, ...], "num_rows": ..., "num_cols": ...} - -Tables in the OCR pipeline -^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Passing ``detect_tables=True`` to :py:meth:`ocr_predictor ` runs table structure -recognition. It relies on the layout model: each region the layout model labels as -a table is cropped and passed to the table model, so a page yields one structured table per detected table region -(``detect_tables=True`` therefore also enables the layout model, whose regions are attached to the page). The words -whose center falls inside a detected cell are regrouped into a structured table, attached to the page as -``page.tables``, and **removed from the regular** ``blocks`` **output** so the same text is not returned twice. Each -table exposes a dense row/column grid that loads directly into pandas. - -.. code:: python3 - - from doctr.io import DocumentFile - from doctr.models import ocr_predictor - - model = ocr_predictor(pretrained=True, detect_tables=True) - doc = DocumentFile.from_images("invoice_with_table.png") - result = model(doc) - - page = result.pages[0] - # Structured tables (one or more per page), kept out of the regular text blocks - for i, table in enumerate(page.tables): - df = table.to_grid() # for a plain list of lists - print(f"Table {i} ({table.num_rows}x{table.num_cols}):") - print(df) - - # The remaining (non-table) text is still available as usual - print(page.render()) - -Tables are included in :meth:`Page.export` under the ``tables`` key, so they are preserved in the JSON export as well. - - -End-to-End OCR --------------- - -The task consists of both localizing and transcribing textual elements in a given image. - -Available OCR architectures -^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -You can use any combination of detection and recognition models supported by docTR. - -For a comprehensive comparison, we have compiled a detailed benchmark on publicly available datasets: - -+---------------------------------------------------------------------------+----------------------------+----------------------------+ -| | FUNSD | CORD | -+===========================================================================+============================+============+===============+ -| **Architecture** | **Recall** | **Precision** | **Recall** | **Precision** | -+---------------------------------------------------------------------------+------------+---------------+------------+---------------+ -| db_resnet50 + crnn_vgg16_bn | 73.37 | 76.11 | 84.80 | 79.09 | -+---------------------------------------------------------------------------+------------+---------------+------------+---------------+ -| db_resnet50 + crnn_mobilenet_v3_small | 73.06 | 75.79 | 84.64 | 78.94 | -+---------------------------------------------------------------------------+------------+---------------+------------+---------------+ -| db_resnet50 + crnn_mobilenet_v3_large | 73.17 | 75.90 | 84.96 | 79.25 | -+---------------------------------------------------------------------------+------------+---------------+------------+---------------+ -| db_resnet50 + master | 73.90 | 76.66 | 85.84 | 80.07 | -+---------------------------------------------------------------------------+------------+---------------+------------+---------------+ -| db_resnet50 + sar_resnet31 | 73.58 | 76.33 | 85.64 | 79.88 | -+---------------------------------------------------------------------------+------------+---------------+------------+---------------+ -| db_resnet50 + vitstr_small | 73.06 | 75.79 | 85.95 | 80.17 | -+---------------------------------------------------------------------------+------------+---------------+------------+---------------+ -| db_resnet50 + vitstr_base | 73.70 | 76.46 | 85.76 | 79.99 | -+---------------------------------------------------------------------------+------------+---------------+------------+---------------+ -| db_resnet50 + parseq | 73.52 | 76.27 | 85.91 | 80.13 | -+---------------------------------------------------------------------------+------------+---------------+------------+---------------+ -| Gvision text detection | 59.50 | 62.50 | 75.30 | 59.03 | -+---------------------------------------------------------------------------+------------+---------------+------------+---------------+ -| Gvision doc. text detection | 64.00 | 53.30 | 68.90 | 61.10 | -+---------------------------------------------------------------------------+------------+---------------+------------+---------------+ -| AWS textract | 78.10 | 83.00 | 87.50 | 66.00 | -+---------------------------------------------------------------------------+------------+---------------+------------+---------------+ -| Azure Form Recognizer (v3.2) | 79.42 | 85.89 | 89.62 | 88.93 | -+---------------------------------------------------------------------------+------------+---------------+------------+---------------+ - - -All OCR models above have been evaluated using both the training and evaluation sets of FUNSD and CORD (cf. :ref:`datasets`). -Explanations about the metrics being used are available in :ref:`metrics`. - -*Disclaimer: both FUNSD subsets combined have 199 pages which might not be representative enough of the model capabilities* - - -Two-stage approaches -^^^^^^^^^^^^^^^^^^^^ -Those architectures involve one stage of text detection, and one stage of text recognition. The text detection will be used to produces cropped images that will be passed into the text recognition block. Everything is wrapped up with :py:meth:`ocr_predictor `. - -.. code:: python3 - - import numpy as np - from doctr.models import ocr_predictor - model = ocr_predictor('db_resnet50', 'crnn_vgg16_bn', pretrained=True) - input_page = (255 * np.random.rand(800, 600, 3)).astype(np.uint8) - out = model([input_page]) - - -You can pass specific boolean arguments to the predictor: - -* `assume_straight_pages`: if you work with straight documents only, it will fit straight bounding boxes to the text areas. -* `preserve_aspect_ratio`: if you want to preserve the aspect ratio of your documents while resizing before sending them to the model. -* `symmetric_pad`: if you choose to preserve the aspect ratio, it will pad the image symmetrically and not from the bottom-right. - -Those 3 are going straight to the detection predictor, as mentioned above (in the detection part). - -Additional arguments which can be passed to the `ocr_predictor` are: - -* `export_as_straight_boxes`: If you work with rotated and skewed documents but you still want to export straight bounding boxes and not polygons, set it to True. -* `straighten_pages`: If you want to straighten the pages before sending them to the detection model, set it to True. -* `detect_orientation`: If you want to estimate the general page orientation and add it to each page, set it to True. -* `detect_language`: If you want to predict the language of the text on each page, set it to True. -* `detect_layout`: If you want to run a layout detection model on each page and attach the detected regions to each page, set it to True (default: False). -* `layout_arch`: The layout architecture name (e.g. ``'lw_detr_s'``, ``'lw_detr_m'``) or your own (fine-tuned) layout model instance to use when ``detect_layout=True``. - -For instance, this snippet instantiates an end-to-end ocr_predictor working with rotated documents, which preserves the aspect ratio of the documents, and returns polygons: - -.. code:: python3 - - from doctr.models import ocr_predictor - model = ocr_predictor('linknet_resnet18', pretrained=True, assume_straight_pages=False, preserve_aspect_ratio=True) - - -Additionally, you can change the batch size of the underlying detection and recognition predictors to optimize the performance depending on your hardware: - -* `det_bs`: batch size for the detection model (default: 2) - will also be used for the layout model if ``detect_layout=True`` -* `reco_bs`: batch size for the recognition model (default: 128) - -.. code:: python3 - - from doctr.models import ocr_predictor - model = ocr_predictor(pretrained=True, det_bs=4, reco_bs=1024) - -To modify the output structure you can pass the following arguments to the predictor which will be handled by the underlying `DocumentBuilder`: - -* `resolve_lines`: whether words should be automatically grouped into lines (default: True) -* `resolve_blocks`: whether lines should be automatically grouped into blocks (default: False) -* `paragraph_break`: relative length of the minimum space separating paragraphs (default: 0.035) - -For example to disable the automatic grouping of lines into blocks: - -.. code:: python3 - - from doctr.models import ocr_predictor - model = ocr_predictor(pretrained=True, resolve_blocks=False) - - -Detecting the document layout -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -In addition to running the :py:meth:`layout_predictor ` standalone, you can plug a layout detection model directly into the end-to-end pipeline by setting ``detect_layout=True``. The detected regions (e.g. Title, Text, Table, Page-header, Page-footer) are attached to every :class:`Page ` and can be accessed through ``page.layout``, exported alongside the rest of the page, and rendered with :py:meth:`show `. - -.. code:: python3 - - from doctr.io import DocumentFile - from doctr.models import ocr_predictor - - model = ocr_predictor(pretrained=True, detect_layout=True) - doc = DocumentFile.from_images("path/to/your/doc.jpg") - result = model(doc) - - # Access the detected layout regions of the first page - for region in result.pages[0].layout: - print(region.type, region.confidence, region.geometry) - - # The layout is part of the exported representation - export = result.pages[0].export() - print(export["layout"]) - - # Overlay both text and layout regions (use display_layout=False to hide the regions) - result.pages[0].show() - -The same ``detect_layout`` / ``layout_arch`` arguments are available for the :py:meth:`kie_predictor `. - -The figures found by the layout model also take part in the reading order, and the Markdown / AsciiDoc / HTML exports can render them, inlined as base64 data URIs or written to an image directory (see :ref:`Figures`): - -.. code:: python3 - - # A self-contained Markdown file, figures included - markdown = result.export_as_markdown(images="embedded") - - # ... or one referencing the crops written to an `assets` directory - from doctr.io import FigureEncoder - - markdown = result.export_as_markdown(images=FigureEncoder("referenced", image_dir="assets")) - -By default (``images="placeholder"``), a comment marks each figure, and ``images="none"`` leaves them out. In the ``"embedded"`` and ``"referenced"`` modes, the text recognized inside a figure is left out of the export, since the image shows it. - - -Running the predictors on GPU -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -You can run the predictors on GPU by specifying the appropriate device. - -Here's how to do it for both **NVIDIA** and **Apple Silicon (MPS)** GPUs: - -.. code:: python3 - - import torch - from doctr.models import ocr_predictor - - # For NVIDIA GPU - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - predictor = ocr_predictor(pretrained=True).to(device) - # Alternatively: predictor = ocr_predictor(pretrained=True).cuda() - - # For Apple Silicon (MPS) - device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu') - predictor = ocr_predictor(pretrained=True).to(device) - - -The same approach applies to all standalone predictors: - -* `recognition_predictor` -* `detection_predictor` -* `crop_orientation_predictor` -* `page_orientation_predictor` -* `layout_predictor` - -Just create the predictor instance and move it to the appropriate device. -To enable **half-precision inference**, append `.bfloat16()` after moving the predictor to the -device -- or `.half()` for FP16, though BF16 is preferred (see :ref:`half-precision` for details) - - -What should I do with the output? -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The ocr_predictor returns a `Document` object with a nested structure (with `Page`, `Block`, `Line`, `Word`, `Artefact`). -When ``detect_layout=True`` was passed, each `Page` additionally carries a list of `LayoutElement` regions under ``page.layout``. -To get a better understanding of our document model, check our :ref:`document_structure` section - -Here is a typical `Document` layout:: - - Document( - (pages): [Page( - dimensions=(340, 600) - (blocks): [Block( - (lines): [Line( - (words): [ - Word(value='No.', confidence=0.91), - Word(value='RECEIPT', confidence=0.99), - Word(value='DATE', confidence=0.96), - ] - )] - (artefacts): [] - )] - )] - ) - -To get only the text content of the `Document`, you can use the `render` method:: - - text_output = result.render() - -For reference, here is the output for the `Document` above:: - - No. RECEIPT DATE - -You can also export them as a nested dict, more appropriate for JSON format:: - - json_output = result.export() - -For reference, here is the export for the same `Document` as above:: - - { - 'pages': [ - { - 'page_idx': 0, - 'dimensions': (340, 600), - 'orientation': {'value': None, 'confidence': None}, - 'language': {'value': None, 'confidence': None}, - 'blocks': [ - { - 'geometry': ((0.1357421875, 0.0361328125), (0.8564453125, 0.8603515625)), - 'lines': [ - { - 'geometry': ((0.1357421875, 0.0361328125), (0.8564453125, 0.8603515625)), - 'words': [ - { - 'value': 'No.', - 'confidence': 0.914085328578949, - 'geometry': ((0.5478515625, 0.06640625), (0.5810546875, 0.0966796875)), - 'objectness_score': 0.96, - 'crop_orientation': {'value': 0, 'confidence': None}, - }, - { - 'value': 'RECEIPT', - 'confidence': 0.9949972033500671, - 'geometry': ((0.1357421875, 0.0361328125), (0.51171875, 0.1630859375)), - 'objectness_score': 0.99, - 'crop_orientation': {'value': 0, 'confidence': None}, - }, - { - 'value': 'DATE', - 'confidence': 0.9578408598899841, - 'geometry': ((0.1396484375, 0.3232421875), (0.185546875, 0.3515625)), - 'objectness_score': 0.99, - 'crop_orientation': {'value': 0, 'confidence': None}, - } - ] - } - ], - 'artefacts': [] - } - ] - } - ] - } - -To export the output as XML (hocr-format) you can use the `export_as_xml` method: - -.. code-block:: python - - xml_output = result.export_as_xml() - for output in xml_output: - xml_bytes_string = output[0] - xml_element = output[1] - -For reference, here is a sample XML byte string output: - -.. code-block:: xml - - - - - docTR - hOCR - - - - - -
        -
        -

        - - Hello - XML - World - -

        -
        -
        - - - -To export the output in reading order as Markdown, AsciiDoc, HTML or XML, you can use the `export_as_markdown`, -`export_as_asciidoc`, `export_as_html` and `export_as_xml` methods (also available on a single :class:`Page`). -Reading order is always applied by these exporters: the content is linearized column by column, the reading direction is inferred from the -recognized text (e.g. right-to-left for Arabic or Hebrew documents), and - when the predictor is run with -layout detection (``detect_layout=True``) - the layout regions are used to render headings, list items and -recognized tables, and to place the page furniture (headers, footers, footnotes): - -.. code-block:: python - - markdown_output = result.export_as_markdown() - asciidoc_output = result.export_as_asciidoc() - html_output = result.export_as_html() - xml_output = result.export_as_xml() - raw_text_output = result.render() # same as result.export_as("text") - dict_output = result.export() # same as result.export_as("json") - -The `export_as` method is a convenience dispatcher over all the formats above:: - - result.export_as("markdown") # or "md" - result.export_as("asciidoc") # or "adoc" - result.export_as("html") - result.export_as("text") # same as render() - result.export_as("json") # same as export() - result.export_as("xml") # same as export_as_xml() - -The document structure itself can also be produced in reading order by building the predictor with -``keep_reading_order=True``, which sorts the blocks of every page in reading order:: - - predictor = ocr_predictor(pretrained=True, keep_reading_order=True) - -Advanced options -^^^^^^^^^^^^^^^^ -We provide a few advanced options to customize the behavior of the predictor to your needs: - -* Modify the binarization threshold for the detection model. -* Modify the box threshold for the detection model. - -This is useful to detect (possible less) text regions more accurately with a higher threshold, or to detect more text regions with a lower threshold. - - -.. code:: python3 - - import numpy as np - from doctr.models import ocr_predictor - predictor = ocr_predictor('db_resnet50', 'crnn_vgg16_bn', pretrained=True) - - # Modify the binarization threshold and the box threshold - predictor.det_predictor.model.postprocessor.bin_thresh = 0.5 - predictor.det_predictor.model.postprocessor.box_thresh = 0.2 - - input_page = (255 * np.random.rand(800, 600, 3)).astype(np.uint8) - out = predictor([input_page]) - - -* Modify how the word confidences are aggregated. - -The character probabilities of the recognition model are aggregated into the word confidence with `"mean"`, `"min"`, -`"max"`, `"median"`, `"geometric_mean"`, `"harmonic_mean"` or a Python callable taking the -1D array of probabilities and returning a float. The default is `"min"` for the CRNN, VIPTR, SAR and MASTER models and -`"mean"` for the ViTSTR and PARSeq models. -A mean can hide a single uncertain character: for `[0.99, 0.98, 0.12, 0.99]`, the mean is `0.77`, the geometric mean -`0.58`, the harmonic mean `0.35` and the minimum `0.12`. `"max"` and `"median"` ignore most uncertain characters, so -they hardly separate the misread words from the correct ones. - -Only the probabilities of the predicted characters are aggregated: the CTC blank frames (CRNN, VIPTR) and the -positions from the end-of-sequence token onwards (SAR, MASTER) are ignored, a CTC character has the highest -probability of its frames, and an empty prediction has a confidence of `0`. The parts of a split wide crop are -aggregated with the `split_confidence_aggregation` of the recognition predictor (`"min"` by default). - -Pass `confidence_aggregation` to `ocr_predictor`, `kie_predictor` or `recognition_predictor` (a model instance passed -to them is modified), or change it afterwards on the postprocessor of the recognition model. - -.. code:: python3 - - from doctr.models import ocr_predictor, parseq - - # Aggregate the character probabilities into the word confidence with the mean - predictor = ocr_predictor(pretrained=True, confidence_aggregation="mean") - - # Or modify the aggregation method of the recognition model afterwards - predictor = ocr_predictor('db_resnet50', 'crnn_vgg16_bn', pretrained=True) - predictor.reco_predictor.model.postprocessor.confidence_aggregation = "geometric_mean" - - # Modify the aggregation of the confidences of the parts of a split wide crop - predictor.reco_predictor.split_confidence_aggregation = "mean" - - # Or set it when instantiating a model - predictor = ocr_predictor('db_resnet50', parseq(pretrained=True, confidence_aggregation="min"), pretrained=True) - - -* Disable page orientation classification - -If you deal with documents which contains only small rotations (~ -45 to 45 degrees), you can disable the page orientation classification to speed up the inference. - -This will only have an effect with `assume_straight_pages=False` and/or `straighten_pages=True` and/or `detect_orientation=True`. - -.. code:: python3 - - from doctr.models import ocr_predictor - model = ocr_predictor(pretrained=True, assume_straight_pages=False, disable_page_orientation=True) - - -* Disable crop orientation classification - -If you deal with documents which contains only horizontal text, you can disable the crop orientation classification to speed up the inference. - -This will only have an effect with `assume_straight_pages=False` and/or `straighten_pages=True`. - -.. code:: python3 - - from doctr.models import ocr_predictor - model = ocr_predictor(pretrained=True, assume_straight_pages=False, disable_crop_orientation=True) - - -* Add a hook to the `ocr_predictor` to manipulate the location predictions before the crops are passed to the recognition model. - -.. code:: python3 - - from doctr.models import ocr_predictor - - class CustomHook: - def __call__(self, loc_preds): - # Manipulate the location predictions here - # 1. The output structure needs to be the same as the input location predictions - # 2. Be aware that the coordinates are relative and needs to be between 0 and 1 - return loc_preds - - my_hook = CustomHook() - - predictor = ocr_predictor(pretrained=True) - # Add a hook in the middle of the pipeline - predictor.add_hook(my_hook) - # You can also add multiple hooks which will be executed sequentially - for hook in [my_hook, my_hook, my_hook]: - predictor.add_hook(hook) - - -* Restrict the recognition model to a subset of its vocabulary. - -If you only expect text from one or more known languages, you can whitelist the corresponding vocabs so the -recognition model can no longer predict any character outside of them. This works with every recognition -architecture and with any predictor wrapping one (`ocr_predictor`, `kie_predictor`, `recognition_predictor`). -A whitelist can only restrict a model to characters it already knows: characters that are not part of the -model's own vocabulary are silently ignored, so make sure the model was trained on a vocab that covers the -languages you need (e.g. a multilingual model). - -.. code:: python3 - - from doctr.datasets import VOCABS - from doctr.io import DocumentFile - from doctr.models import ocr_predictor - from doctr.models.utils import add_whitelist - - predictor = ocr_predictor(pretrained=True) - - # The recognition model can now only predict Polish/German characters - handle = add_whitelist(predictor, [VOCABS["polish"], VOCABS["german"]]) - - input_page = DocumentFile.from_images("path/to/your/image.png") - out = predictor(input_page) - - # Restore the original, unconstrained decoding - handle.remove() - -The returned handle can also be used as a context manager, in which case the whitelist is removed on exit: - -.. code:: python3 - - with add_whitelist(predictor, VOCABS["german"]): - out = predictor(input_page) # only German characters can be predicted here - # the whitelist is automatically removed outside of the ``with`` block - -By default forbidden characters are dropped (``strategy="mask"``), so decoding falls back to the highest-scoring -allowed character. Alternatively, ``strategy="nearest"`` folds each forbidden character onto the closest allowed -one (e.g. ``ä`` -> ``a``, ``ł`` -> ``l``), which is useful to normalize accents/diacritics onto a base alphabet. -The mapping is built by transliteration by default; pass ``mapping="weights"`` to derive it from the model's own -learned confusions, or a ``{forbidden_char: allowed_char}`` dict to override specific characters. - -.. code:: python3 - - from doctr.datasets import VOCABS - from doctr.models import ocr_predictor - from doctr.models.utils import add_whitelist - - predictor = ocr_predictor(pretrained=True) - - # Fold any non-ASCII character onto its closest ASCII letter (e.g. é -> e, ł -> l) - handle = add_whitelist(predictor, VOCABS["latin"], strategy="nearest") - out = predictor(input_page) - handle.remove() diff --git a/doctr/utils/metrics.py b/doctr/utils/metrics.py index 4d20bc7078..909074a858 100644 --- a/doctr/utils/metrics.py +++ b/doctr/utils/metrics.py @@ -661,8 +661,7 @@ class ObjectDetectionMetric: - :math:`\mathcal{C}` is the set of possible class indices, - :math:`S` are confidence scores associated to predictions, - :math:`\mathcal{T} = \{0.5, 0.55, \dots, 0.95\}` is the set of IoU thresholds, - - :math:`AP_t(c)` is the Average Precision for class :math:`c` - at IoU threshold :math:`t`. + - :math:`AP_t(c)` is the Average Precision for class :math:`c` at IoU threshold :math:`t`. For a given class and IoU threshold, predictions from all images are aggregated and sorted globally by decreasing confidence score. diff --git a/pyproject.toml b/pyproject.toml index b50f62111c..2c726eaa21 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -81,14 +81,16 @@ quality = [ "pre-commit>=2.17.0", ] docs = [ - "sphinx>=3.0.0,!=3.5.0", - "sphinxemoji>=0.1.8", - "sphinx-copybutton>=0.3.1", + "sphinx>=8.0.0", "docutils<0.23", - "recommonmark>=0.7.1", - "sphinx-markdown-tables>=0.0.15", + "pydata-sphinx-theme>=0.23.0", + "myst-parser>=4.0.0", + "sphinx-design>=0.6.0", + "sphinx-copybutton>=0.5.2", + "sphinx-reredirects>=0.1.5", + # Only used to render the documentation of previous releases (cf. docs/build.sh) + "sphinxemoji>=0.1.8", "sphinx-tabs>=3.3.0", - "furo>=2022.3.4", ] dev = [ # PyTorch @@ -110,14 +112,16 @@ dev = [ "mypy>=1.0", "pre-commit>=3.0.0", # Docs - "sphinx>=3.0.0,!=3.5.0", - "sphinxemoji>=0.1.8", - "sphinx-copybutton>=0.3.1", + "sphinx>=8.0.0", "docutils<0.23", - "recommonmark>=0.7.1", - "sphinx-markdown-tables>=0.0.15", + "pydata-sphinx-theme>=0.23.0", + "myst-parser>=4.0.0", + "sphinx-design>=0.6.0", + "sphinx-copybutton>=0.5.2", + "sphinx-reredirects>=0.1.5", + # Only used to render the documentation of previous releases (cf. docs/build.sh) + "sphinxemoji>=0.1.8", "sphinx-tabs>=3.3.0", - "furo>=2022.3.4", ] [project.urls]
        - - Star - -