# A Survey on Deep learning based Document Image Enhancement

arXiv: https://arxiv.org/abs/2112.02719 · HTML: https://mlsanity.com/paper/2112.02719/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 75 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://mlsanity.com/paper/1906.06972/) (2019)
- [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017)
- [Deeply-Recursive Convolutional Network for Image Super-Resolution](https://mlsanity.com/paper/1511.04491/) (2015)
- [Detail-revealing Deep Video Super-resolution](https://mlsanity.com/paper/1704.02738/) (2017)
- [DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement](https://mlsanity.com/paper/2010.08764/) (2020)
- [Document Rectification and Illumination Correction using a Patch-based CNN](https://mlsanity.com/paper/1909.09470/) (2019)
- [MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream](https://mlsanity.com/paper/1807.05786/) (2018)
- [Document Image Binarization with Fully Convolutional Neural Networks](https://mlsanity.com/paper/1708.03276/) (2017)
- [A selectional auto-encoder approach for document image binarization](https://mlsanity.com/paper/1706.10241/) (2017)
- [Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation](https://mlsanity.com/paper/1811.09393/) (2018)
- [SauvolaNet: Learning Adaptive Sauvola Network for Degraded Document Binarization](https://mlsanity.com/paper/2105.05521/) (2021)
- [Learning to Clean: A GAN Perspective](https://mlsanity.com/paper/1901.11382/) (2019)
- [End-to-End Unsupervised Document Image Blind Denoising](https://mlsanity.com/paper/2105.09437/) (2021)
- [Enhance to Read Better: A Multi-Task Adversarial Network for Handwritten Document Image Enhancement](https://mlsanity.com/paper/2105.12710/) (2021)
- [Light-weight Document Image Cleanup using Perceptual Loss](https://mlsanity.com/paper/2105.09076/) (2021)
- [Learning Multi-Scale Photo Exposure Correction](https://mlsanity.com/paper/2003.11596/) (2020)
- [Language Independent Single Document Image Super-Resolution using CNN for improved recognition](https://mlsanity.com/paper/1701.08835/) (2017)

## Built on by (most cited first, 7 of 7)

- [NAF-DPM: A Nonlinear Activation-Free Diffusion Probabilistic Model for Document Enhancement](https://mlsanity.com/paper/2404.05669/) (2024)
- [Seal2Real: Prompt Prior Learning on Diffusion Model for Unsupervised Document Seal Data Generation and Realisation](https://mlsanity.com/paper/2310.00546/) (2023)
- [DECDM: Document Enhancement using Cycle-Consistent Diffusion Models](https://mlsanity.com/paper/2311.09625/) (2023)
- [DocShaDiffusion: Diffusion Model in Latent Space for Document Image Shadow Removal](https://mlsanity.com/paper/2507.01422/) (2025)
- [MatteViT: High-Frequency-Aware Document Shadow Removal with Shadow Matte Guidance](https://mlsanity.com/paper/2512.08789/) (2025)
- [A Multistage Extraction Pipeline for Long Scanned Financial Documents: An Empirical Study in Industrial KYC Workflows](https://mlsanity.com/paper/2604.26462/) (2026)
- [From Pixels to Pairs: A Comprehensive Benchmark of LLM-Driven Key-Value Extraction in Noisy Document Settings](https://mlsanity.com/paper/2609.17538/) (2026)

## Abstract (arXiv)

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.