# TTD

**Repository Path**: semikonductor/tapertextdetect

## Basic Information

- **Project Name**: TTD
- **Description**: 123adsas saas 
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-03-03
- **Last Updated**: 2024-05-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# DocTamper
The DocTamper dataset is now avaliable at [BaiduDrive](https://pan.baidu.com/s/1nEEnq1ZWIem7wnkQ1YdTNw?pwd=od9k) and Google Drive ([part1](https://drive.google.com/file/d/150teGvJbtWSULljrh9Sp_NrTlEXKPsTm/view?usp=drive_link) and [part2](https://drive.google.com/file/d/1rJOMEu8c25ZxpWliCXmxRk6wFULZS7Z2/view?usp=share_link)).


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".

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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](https://github.com/DLLXW/data-science-competition/blob/main/tianchi/ImageForgeryLocationChallenge/utils/deeplearning_qyl.py), you just need to adjust the loss functions and the learing rate decay curve. I also used its [augmentation pipline](https://github.com/DLLXW/data-science-competition/blob/main/tianchi/ImageForgeryLocationChallenge/dataset/RSCDataset.py) except for (RandomBrightnessContrast, ShiftScaleRotate, CoarseDropout).


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

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Any question about this work please contact 202221012612@mail.scut.edu.cn.