论文标题
De-Gan：用于文档增强的条件生成对抗网络
DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement
论文作者
论文摘要
文档经常表现出各种形式的降解，这使得很难被读取并实质上恶化了OCR系统的性能。在本文中，我们提出了一个名为文档增强生成对抗网络（DE-GAN）的有效端到端框架，该框架使用条件gan（CGAN）恢复严重降级的文档图像。据我们所知，这种做法尚未在生成的对抗性深网的背景下进行研究。我们证明，在不同的任务中（文档清理，二线化，去除和水印），De-Gan可以以高质量生产降级文档的增强版本。此外，与广泛使用的DIBCO 2013，DIBCO 2017和H-DIBCO 2018数据集的最新方法相比，我们的方法提供了一致的改进，证明了其能够将退化的文档图像恢复到理想状态。在各种降解上获得的结果揭示了在其他文档增强问题中被利用的拟议模型的灵活性。
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.
