A Survey on Deep learning based Document Image Enhancement

TL;DR

This survey reviews recent deep learning methods for enhancing degraded document images, covering tasks like binarization, denoising, and watermark removal, and discusses future research directions in the field.

Contribution

It provides a comprehensive overview of deep learning-based document image enhancement techniques, datasets, metrics, and highlights underexplored tasks and future opportunities.

Findings

Deep learning methods significantly improve document image quality.

Several tasks like watermark removal and shadow correction are underexplored.

The paper identifies promising future research directions.

Abstract

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.