This is an implementation for the paper "Enhance to Read Better: A Multi-Task Adversarial Network for Handwritten Document Image Enhancement" designed to enhance the document quality before the recognition process. It could be used for document cleaning and binarization.
• A Generative Adversarial Network for handwritten document image binarization.
• We perform document binarization while ensuring text readability, simultaneously, by integrating a handwritten text recognition component within the proposed architecture.
• The proposed model enhances different forms of documents, independently of the text language.
• We achieve state-of-the-art performance on the public H-DIBCO datasets.
This work is only allowed for academic research use. For commercial use, please contact the author.
install the requirements.txt pip install requirements.txt
python distort_image_khatt.py
python GAN_AHTR.py
python eval_Dibco_2010.py
Ckeckpoints to test using the HDIBCO 2010 are available :
https://usaupload.com/5Guu/discriminator_weights.h5
https://usaupload.com/5GuG/generator_weights.h5
python train_khatt_basic_distorted.py
If this work was useful for you, please cite it as:
@article{KHAMEKHEMJEMNI2022108370, title = {Enhance to read better: A Multi-Task Adversarial Network for Handwritten Document Image Enhancement}, journal = {Pattern Recognition}, volume = {123}, pages = {108370}, year = {2022}, doi = {https://doi.org/10.1016/j.patcog.2021.108370}, author = {Sana {Khamekhem Jemni} and Mohamed Ali Souibgui and Yousri Kessentini and Alicia Fornés}, }