DocUnfold: Leveraging Unfolding Network and A Real-World Large-Scale Dataset for Handwriting Contamination Removal in Documents

Xuhang Chen, Ziyang Zhou, Zimeng Li📮, Xiujun Zhang, Yihang Dong, Kim-Fung Tsang (📮Corresponding author)

Huizhou University, Shenzhen Polytechnic University, University of Macau, SIAT CAS

IEEE Transactions on Consumer Electronics

🔮 Dataset

The HW5K dataset is available at huggingface.

⚙️ Usage

Training

You may download the dataset first, and then specify TRAIN_DIR, VAL_DIR and SAVE_DIR in the section TRAINING in config.yml.

For single GPU training:

python train.py

For multiple GPUs training:

accelerate config
accelerate launch train.py

If you have difficulties with the usage of accelerate, please refer to Accelerate.

Inference

python infer.py

💗 Acknowledgement

This work was supported in part by the National Natural Science Foundation of China (Grant No. 62501412 and 62272313), in part by Shenzhen Medical Research Fund (Grant No. A2503006), in part by Shenzhen Polytechnic University Research Fund (Grant No. 6025310023K) and in part by Guangdong Basic and Applied Basic Research Foundation (Grant No. 2024A1515140010).

🛎 Citation

If you find our work helpful for your research, please cite:

@ARTICLE{11320455,
  author={Chen, Xuhang and Zhou, Ziyang and Li, Zimeng and Zhang, Xiujun and Dong, Yihang and Tsang, Kim-Fung},
  journal={IEEE Transactions on Consumer Electronics}, 
  title={DocUnfold: Leveraging Unfolding Network and A Real-World Large-Scale Dataset for Handwriting Contamination Removal in Documents}, 
  year={2025},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TCE.2025.3649878}
}