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 [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".
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 [email protected].
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.
The original data of the dataset is sourced from public channels such as the Internet, and its copyright shall remain with the original providers. The collated and annotated dataset presented in this case is for non-commercial use only and is currently licensed to universities and research institutions. To apply for the use of this dataset, please fill in the corresponding application form in accordance with the requirements specified on the dataset’s official website. The applicant must be a full-time employee of a university or research institute and is required to sign the application form. For the convenience of review, it is recommended to affix an official seal (a seal of a secondary-level department is acceptable).
Any question about this work please contact [email protected].
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}
}