🚀 Exciting update! We have created a demo for our paper, showcasing the adaptive removal capabilities of our method. Check it out here!

DeepEraser

The official code for “DeepEraser: Deep Iterative Context Mining for Generic Text Eraser”.

🚀 Demo (Link)

We have already released the pre-trained model, i.e., $ROOT/deeperaser.pth.

1. Put the distorted images in $ROOT/input_imgs/ and rename it to input.png.
2. Put the mask image in $ROOT/input_imgs/ and rename it to mask.png.
3. Run the script and the processed image is saved in $ROOT/output_imgs/ by default.

   python demo.py
   

Citation

If you find this code useful for your research, please use the following BibTeX entry.

@article{feng2024deeperaser,
  title={DeepEraser: Deep Iterative Context Mining for Generic Text Eraser},
  author={Feng, Hao and Wang, Wendi and Liu, Shaokai and Deng, Jiajun and Zhou, Wengang and Li, Houqiang},
  journal={arXiv preprint arXiv:2402.19108},
  year={2024}
}