ssocean / Attention-U-Net

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Alleviating pseudo-touching in U-Net based binarization approach

code for <Zhao, P., Wang, W., Zhang, G., & Lu, Y. (2021). Alleviating pseudo-touching in attention U-Net-based binarization approach for the historical Tibetan document images. Neural Computing and Applications, 1-12.>

Start with Train.py

python Train.py [imgs directory/] [masks directory/] (dont miss the last '/')

Inference

python Prediction.py [imgs dir] [out_dir] [model_pth] You are welcome to contact me anytime: [e-mail]

About Pseudo-touching (假性粘连)

The optimal magnifications of various data might be different. We suggest trying various magnifications on your own dataset.