Digitized documents such as scientific articles, tax forms, invoices, contract papers, historic texts are widely used nowadays. These document images could be degraded or damaged due to various reasons including poor lighting conditions, shadow, distortions like noise and blur, aging, ink stain, bleed-through, watermark, stamp, etc. Document image enhancement plays a crucial role as a pre-processing step in many automated document analysis and recognition tasks such as character recognition. With recent advances in deep learning, many methods are proposed to enhance the quality of these document images. In this paper, we review deep learning-based methods, datasets, and metrics for six main document image enhancement tasks, including binarization, debluring, denoising, defading, watermark removal, and shadow removal. We summarize the recent works for each task and discuss their features, challenges, and limitations. We introduce multiple document image enhancement tasks that have received little to no attention, including over and under exposure correction, super resolution, and bleed-through removal. We identify several promising research directions and opportunities for future research.

翻译：科学文章、税务表格、发票、合同文件、历史文献等数字化文件如今被广泛使用,这些文件图像可能由于各种原因,包括照明条件差、阴影、噪音和模糊、老化、墨水污、流血、水印、印章等扭曲而退化或损坏。文件图像的增强作为许多自动文件分析和识别任务(如品格识别)的处理前步骤,发挥着关键作用。随着最近深层次学习的进展,提出了许多提高这些文件图像质量的方法。在本文件中,我们审查了六种主要文件图像增强任务的深层次学习方法、数据集和衡量标准,包括二进制、拆卸、拆卸、拆卸、去除水印、去除阴影等。我们总结了每项任务的最新工作,并讨论了其特征、挑战和限制。我们提出了多个文件图像增强任务,这些任务很少受到注意,包括暴露校正、超级分辨率和流血清除。我们确定了若干有希望的研究方向和今后研究机会。