DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement
Mohamed Ali Souibgui,
Yousri Kessentini
Abstract: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 diffe… Show more
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