  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.
