GDB: Gated convolutions-based Document Binarization

Description

This is an official implementation for the paper GDB: Gated convolutions-based Document Binarization.

This repository also comprehensively collects the datasets that may be used in document binarization.

Datasets

Below is a summary table of the datasets used for document binarization, along with links to download them.

Environment

• Python >= 3.7
• torch >= 1.7.0
• torchvision >= 0.8.0

Usage

Prepare the dataset

Note: The pre-processing code is not provided yet. But it is on the way.

You can download the datasets from the links below and put them in the datasets_ori folder. When evaluating performance on the DIBCO2019 dataset, first gather all datasets except for DIBCO2019 and place them in the img and gt folders under the datasets_ori directory. Then crop the images and ground truth images into patches (256 * 256) and place them in the img and gt folders under the datasets/DIBCO2019 directory. Next, use the Otsu thresholding method to binaryze the images under datasets/img and place the results in the datasets/otsu folder. Use the Sobel operator to process the images under datasets/img and place the results in the datasets/sobel folder. With these preprocessing steps completed, Pass ./datasets/img as an argument for the --dataRoot parameter in train.py and begin training.

Training

python train.py

Testing

python test.py

Datasets

| Dataset | |--------------------------------------------------------------------------------------------------------------------------------------------------------------| | DIBCO 2009 | | H-DIBCO 2010 | | DIBCO 2011 | | H-DIBCO 2012 | | DIBCO 2013 | | H-DIBCO 2014 | | H-DIBCO 2016 | | DIBCO 2017 | | H-DIBCO 2018 | | DIBCO 2019 | | Palm Leaf Manuscript | | Persian Heritage Image Binarization Dataset (PHIBD) | | Ensiedeln | | Noisy Office | | Synchromedia Multispectral dataset | | Bickly-diary dataset | | IAM Historical Document Database |

To-do list

• [x] Add the code for training
• [x] Add the code for testing
• [ ] Add the code for pre-processing
• [ ] Restruct the code
• [ ] Upload the pretrained weights
• [x] Comprehensively collate document binarization benchmark datasets
• [ ] Add the code for evaluating the performance of the model

License

This work is permitted for academic research purposes only. For commercial use, please contact the author.

Citation

• If this work is useful, please cite it as:

@article{yang2024gdb,
  title={GDB: gated convolutions-based document binarization},
  author={Yang, Zongyuan and Liu, Baolin and Xiong, Yongping and Wu, Guibin},
  journal={Pattern Recognition},
  volume={146},
  pages={109989},
  year={2024},
  publisher={Elsevier}
}