update readme

Author: j-rausch

README.md

@@ -3,8 +3,62 @@
 [Updated paper](docparser.pdf)
 
 
+### Installation and requirements
+
+Tested for Ubuntu 18.04/20.04.
+
+Use of a GPU significantly speeds up generation of detection outputs, but it is possible to run the inference demo code on CPU.
+
+To setup via Anaconda, please follow these steps:
+
+1. Install anaconda. Up-to-date instructions can be found at: https://docs.anaconda.com/anaconda/install/
+
+2. Set up python 3.6 environment: 
+`conda create -n docparser python=3.6`
+
+3. Activate the environment:
+`source activate docparser`
+
+4. Install all requirements:
+`pip install -r requirements.txt`
+	- (for GPU-enabled installation: `pip install -r requirements_gpu.txt`)
+
+
+5. Install Mask R-CNN library:
+    - We used a slightly modified version of https://github.com/matterport/Mask_RCNN, though the original version should still be usable, possibly with minor adaptions.
+    - Clone repository from https://github.com/j-rausch/Mask_RCNN 
+	- Change into mask rcnn directory 
+	- type `python setup.py develop`
+
+6. Install docparser:
+	- Change into DocParser directory 
+	- type `python setup.py develop`
+
+7. Prepare the datasets:
+	- Download arxivdocs-target and ICDAR files as shown on https://github.com/DS3Lab/arXivDocs
+	- Extract datasets to the `DocParser` subdirectory 
+		- (resulting in structure: `DocParser/datasets`). 
+
+8. Prepare the trained models:
+	- Download from URL:
+	- Extract the pretrained models to the `default_models` subdirectory in `DocParser/docparser/`
+		- (resulting in structure `DocParser/docparser/default_models/`).
+    - For convenience, we include the COCO pre-trained weights from from https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5 in the zip file
+
+9. For running the ICDAR demo:
+	- Please note that, in order to run the ICDAR 2013 evaluation script provided by the competition organizers, a Java installation is necessary. We used `openjdk 11.0.7 2020-04-14` in our experiments. 
+	- If necessary, update permissions for the evaluation script (on linux systems):
+		`chmod a+x DocParser/docparser/utils/dataset-tools-20180206.jar`
+
+
+10. From the `DocParser` directory, execute:
+`python demos/demo_inference.py` plus one or more of the following command line arguments:
+	
+	- `--page`
+	- `--table`
+	- `--icdar`
+	- e.g. `python demos/demo_inferencey.py --page --table`
 
-TBA
 
 ### Evaluations