Good news! Our new work exhibits state-of-the-art performances on DocUNet benchmark dataset: DocScanner: Robust Document Image Rectification with Progressive Learning

DocTr

DocTr: Document Image Transformer for Geometric Unwarping and Illumination Correction
ACM MM 2021 Oral

Any questions or discussions are welcomed!

Training

• For geometric unwarping, we train the GeoTr network using the Doc3d dataset.
• For illumination correction, we train the IllTr network based on the DRIC dataset.

Inference

1. Download the pretrained models here and put them to $ROOT/model_pretrained/.
2. Geometric unwarping:

   python inference.py
   

3. Geometric unwarping and illumination rectification:

   python inference.py --ill_rec True
   

Evaluation

• We use the same evaluation code as DocUNet benchmark dataset based on Matlab 2019a.
• Please compare the scores according to your Matlab version.
• Use the images available here for reproducing the quantitative performance reported in the paper and further comparison.

Citation

If you find this code useful for your research, please use the following BibTeX entry.

@inproceedings{feng2021doctr,
  title={DocTr: Document Image Transformer for Geometric Unwarping and Illumination Correction},
  author={Feng, Hao and Wang, Yuechen and Zhou, Wengang and Deng, Jiajun and Li, Houqiang},
  booktitle={Proceedings of the 29th ACM International Conference on Multimedia},
  pages={273--281},
  year={2021}
}

@article{feng2021docscanner,
  title={DocScanner: Robust Document Image Rectification with Progressive Learning},
  author={Feng, Hao and Zhou, Wengang and Deng, Jiajun and Tian, Qi and Li, Houqiang},
  journal={arXiv preprint arXiv:2110.14968},
  year={2021}
}