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Arabic-Document-Analysis-Using-Deep-Learning-Structure

Source codes of our CS591 project: Arabic Document Analysis Using Deep Learning Structure.

Wenda Qin, Hao Yu

Faster-R-CNN-based method

A pre-trained model can be downloaded here: http://cs-people.bu.edu/wdqin/current_model_nms_05

  • training the model: run Faster-RCNN_based_and_CRAFT_based_processing/creating_masks_for_BCE.ipynb then run Faster-RCNN_based_and_CRAFT_based_processing/training_detection_model.ipynb you will need to modify the dataset size in the code as the default number of images in the code is 300.
  • to watch testing results generated by the model: run Faster-RCNN_based_and_CRAFT_based_processing/Visualizing_the_model.ipynb, you need to specify the model you trained or the pre-trained one. run Faster-RCNN_based_and_CRAFT_based_processing/Conversion_of_Format.ipynb the visualized results are shown in Faster-RCNN_based_and_CRAFT_based_processing/results (you might need to change the path correctly before doing that)

CRAFT-based method

The implementation of CRAFT-based method are in the folder craft and segmentation

Requirements

  • PyTorch>=0.4.1
  • torchvision>=0.2.1
  • opencv-python>=3.4.2
  • requirments.txt
pip install -r craft/requirements.txt

Generating text detection results

  • Download the trained model which is trained on SynthText, IC13, IC17 dataset.

  • Run with pretrained model

python craft/test.py --trained_model=[weightfile] --test_folder=[folder path to test images]

The result images will be saved to ./result by default.

Segmentation

  • Train
python segmentation/main.py --train --input=[folder path to input images] --output=[folder path to output files]
  • Test
python segmentation/main.py --input=[folder path to input images] --output=[folder path to output files]
  • Merging non-text results from Faster-R-CNN-based method: run CRAFT_generate_csv.ipynb run Merging_Text_and_non_text_csvs.ipynb

To visualize the result, run Faster-RCNN_based_and_CRAFT_based_processing/Conversion_of_Format.ipynb the visualized results are shown in Faster-RCNN_based_and_CRAFT_based_processing/results (you might need to change the path correctly before doing that)