This is the official repository of the paper Towards Robust Tampered Text Detection in Document Image: New dataset and New Solution. paper.
The DocTamper dataset is now avaliable at BaiduDrive and Kaggle.
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 202221012612@mail.scut.edu.cn 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".
The official implementation of the paper Towards Robust Tampered Text Detection in Document Image: New Dataset and New Solution is in the "models" directory.
Open Source Scheme:
1、Inference models and code
2、Training code: contact 202221012612@mail.scut.edu.cn.
The evalution metrics and training code are built upon this repo.
The DocTamper dataset does not cover AIGC text tampering, but such a scenario is sufficiently covered by our new work.
Any question about this work please contact 202221012612@mail.scut.edu.cn.
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}
}