TY  - JOUR
AU  - Mohamed Ali Souibgui
AU  - Y.Kessentini
PY  - 2022//
TI  - DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement
T2  - TPAMI
JO  - IEEE Transactions on Pattern Analysis and Machine Intelligence
SP  - 1180
EP  - 1191
VL  - 44
IS  - 3
N2  - Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images. To the best of our knowledge, this practice has not been studied within the context of generative adversarial deep networks. We demonstrate that, in different tasks (document clean up, binarization, deblurring and watermark removal), DE-GAN can produce an enhanced version of the degraded document with a high quality. In addition, our approach provides consistent improvements compared to state-of-the-art methods over the widely used DIBCO 2013, DIBCO 2017 and H-DIBCO 2018 datasets, proving its ability to restore a degraded document image to its ideal condition. The obtained results on a wide variety of degradation reveal the flexibility of the proposed model to be exploited in other document enhancement problems.
UR  - https://ieeexplore.ieee.org/document/9187695
L1  - http://refbase.cvc.uab.es/files/SoK2020.PDF
UR  - http://dx.doi.org/10.1109/TPAMI.2020.3022406
N1  - DAG; 602.230; 600.121; 600.140
ID  - Mohamed Ali Souibgui2022
ER  -