Abstract
Digitized documents such as scientific articles, tax forms, invoices, contract papers, and historic texts, are widely used nowadays. These images could be degraded or damaged due to various reasons including poor lighting conditions when capturing the image, shadow while scanning them, distortion like noise and blur, aging, ink stain, bleed through, watermark, stamp, etc. Document image enhancement and restoration play a crucial role in many automated document analysis and recognition tasks, such as content extraction using optical character recognition (OCR). 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 different document image enhancement problems. We provide a comprehensive overview of deep learning-based methods for six different document image enhancement tasks, including binarization, debluring, denoising, defading, watermark removal, and shadow removal. We summarize the main state-of-the-art works for each task and discuss their features, challenges, and limitations. We introduce multiple document image enhancement tasks that have received no to little attention, including over and under exposure correction and bleed-through removal, and identify several other promising research directions and opportunities for future research.
Abstract (translated)
URL
https://arxiv.org/abs/2112.02719
