paper

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 install

6. Install docparser:

   • Change into DocParser directory
   • type python setup.py develop

7. Prepare the datasets:

   • Download arxivdocs-target from: https://drive.google.com/file/d/1qVR_mI2xBtgsUG0LWVJctygDzvkMYN7C/view?usp=sharing
   • To run the ICDAR demo, download the prepared files from: https://drive.google.com/file/d/1SdGTq80eUGqUJBA6kdVQBO9L6a_ijAcN/view?usp=sharing
   • Extract datasets to the DocParser subdirectory
     • (resulting in structure: DocParser/datasets).

8. Prepare the trained models:

   • Download from URL: https://drive.google.com/file/d/1Hi4-tg4Zmtx8zYiCg6IBi47R88PdmAW4/view?usp=sharing
   • 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_inference.py --page --table

The results of our current system on arXivDocs-target is likely to perform better than the one evaluated in the last version of the paper, mostly due to further improvements to postprocessing.

Updated Results. We corrected a read-out error on the outputs of the provided evaluation script for documents with multiple tables.

(PDF-based system F1: 0.9221)

Parts of our code is based on: https://github.com/rafaelpadilla/Object-Detection-Metrics

https://github.com/matterport/Mask_RCNN

Rausch, J., Martinez, O., Bissig, F., Zhang, C., & Feuerriegel, S. - 35th AAAI Conference on Artificial Intelligence (AAAI-21)(virtual)