这里是论文TextDiff: Mask-Guided Residual Diffusion Models for Scene Text Image Super-Resolution的官方复现仓库。TextDiff是一个场景文字超分辨率优化模型（详见论文).
- 置顶: 介绍一款我们实验室开发的多功能且多平台的OCR软件,包含常用的各种OCR功能，例如PDF转word，PDF转excel，公式识别，表格识别以及自动去除水印功能，欢迎试用！
- 查看To-do lists，获取最新信息。
- python >= 3.7
- pytorch >= 1.7.0
- torchvision >= 0.8.0
- lmdb >= 0.98
- pillow >= 7.1.2
- numpy
- six
- tqdm
- python-opencv
- easydict
- yaml
- 安装
git clone https://github.com/Lenubolim/TextDiff.git
-
参数配置
见config.yaml文件 -
训练
python train.py
python test.py
- 添加训练代码(To be released soon.)
- 添加推理代码(To be released soon.)
- 使用DPM_solver减少推理步长
- 如果你觉得TextDiff对你有帮助，请给个star，谢谢！
- 如果你有任何问题，欢迎提issue(issue通知与我邮箱绑定，看到后我会及时回复)。
- 如果你愿意将TextDiff作为你的项目的baseline，欢迎引用我们的论文。
- [1] Scene text telescope: Text-focused scene image super-resolution
- [2] Activating more pixels in image super-resolution transformer.
- [3] Srdiff: Single image super-resolution with diffusion probabilistic models.
- [4] DocDiff: Document Enhancement via Residual Diffusion Models
- [5] Improving Scene Text Image Super-Resolution via Dual Prior Modulation Network
If you use (part of) my code or find my work helpful, please consider citing
@article{liu2023textdiff,
title={TextDiff: Mask-Guided Residual Diffusion Models for Scene Text Image Super-Resolution},
author={Liu, Baolin and Yang, Zongyuan and Wang, Pengfei and Zhou, Junjie and Liu, Ziqi and Song, Ziyi and Liu, Yan and Xiong, Yongping},
journal={arXiv preprint arXiv:2308.06743},
year={2023}
}
This code is developed relying on DocDiff and TATT. Thanks for these great projects. Among them, DocDiff is the main research content of my classmate, and I participated in part of the research.
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