nomiluks / doctamper

[CVPR2023] Towards Robust Tampered Text Detection in Document Image: New Dataset and New Solution

DocTamper

The DocTamper dataset is now avaliable at BaiduDrive and Google Drive (part1 and part2).

The DocTamper dataset is only available for non-commercial use, you can request a password for it by sending an email with education email to [email protected] explaining the purpose.

To visualize the images and their corresponding ground-truths from the provided .mdb files, you can run this command "python vizlmdb.py --input DocTamperV1-FCD --i 0".

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The official implementation of the paper Towards Robust Tampered Text Detection in Document Image: New Dataset and New Solution is in the "models" directory.

I delay the release of training codes as forced by my supervisor and the cooperative enterprise who bought them. My training pipline for DocTamper dataset and the IoU metric heavily brought from a famous project in this area, the results of the paper can be easily re-produced with it, you just need to adjust the loss functions and the learing rate decay curve. I also used its augmentation pipline except for (RandomBrightnessContrast, ShiftScaleRotate, CoarseDropout).

Open Source Scheme:
1、Inference models and codes: June, 2023.
2、Training codes: TBD.
3、Data synthesis code: Within 2024.

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Any question about this work please contact [email protected].

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If you find this work useful in your research, please consider citing:

@inproceedings{qu2023towards,
  title={Towards Robust Tampered Text Detection in Document Image: New Dataset and New Solution},
  author={Qu, Chenfan and Liu, Chongyu and Liu, Yuliang and Chen, Xinhong and Peng, Dezhi and Guo, Fengjun and Jin, Lianwen},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5937--5946},
  year={2023}
}