# DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement

arXiv: https://arxiv.org/abs/2010.08764 · HTML: https://mlsanity.com/paper/2010.08764/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2020.

## Built on by (most cited first, 30 of 44)

- [StyleShot: A Snapshot on Any Style](https://mlsanity.com/paper/2407.01414/) (2024)
- [DocDiff: Document Enhancement via Residual Diffusion Models](https://mlsanity.com/paper/2305.03892/) (2023)
- [OCR-IDL: OCR Annotations for Industry Document Library Dataset](https://mlsanity.com/paper/2202.12985/) (2022)
- [DocEnTr: An End-to-End Document Image Enhancement Transformer](https://mlsanity.com/paper/2201.10252/) (2022)
- [DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks](https://mlsanity.com/paper/2405.04408/) (2024)
- [Visual Text Processing: A Comprehensive Review and Unified Evaluation](https://mlsanity.com/paper/2504.21682/) (2025)
- [Transformer-Based Approach for Joint Handwriting and Named Entity Recognition in Historical documents](https://mlsanity.com/paper/2112.04189/) (2021)
- [Text-DIAE: A Self-Supervised Degradation Invariant Autoencoders for Text Recognition and Document Enhancement](https://mlsanity.com/paper/2203.04814/) (2022)
- [High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented Transformer](https://mlsanity.com/paper/2410.22922/) (2024)
- [GDB: Gated convolutions-based Document Binarization](https://mlsanity.com/paper/2302.02073/) (2023)
- [OCRGenBench: A Comprehensive Benchmark for Evaluating OCR Generative Capabilities](https://mlsanity.com/paper/2507.15085/) (2025)
- [SauvolaNet: Learning Adaptive Sauvola Network for Degraded Document Binarization](https://mlsanity.com/paper/2105.05521/) (2021)
- [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)
- [A Survey on Deep learning based Document Image Enhancement](https://mlsanity.com/paper/2112.02719/) (2021)
- [Light-weight Document Image Cleanup using Perceptual Loss](https://mlsanity.com/paper/2105.09076/) (2021)
- [Three-stage binarization of color document images based on discrete wavelet transform and generative adversarial networks](https://mlsanity.com/paper/2211.16098/) (2022)
- [Uni-DocDiff: A Unified Document Restoration Model Based on Diffusion](https://mlsanity.com/paper/2508.04055/) (2025)
- [DocBinFormer: A Two-Level Transformer Network for Effective Document Image Binarization](https://mlsanity.com/paper/2312.03568/) (2023)
- [ST-KeyS: Self-Supervised Transformer for Keyword Spotting in Historical Handwritten Documents](https://mlsanity.com/paper/2303.03127/) (2023)
- [DocStormer: Revitalizing Multi-Degraded Colored Document Images to Pristine PDF](https://mlsanity.com/paper/2310.17910/) (2023)
- [Binarizing Documents by Leveraging both Space and Frequency](https://mlsanity.com/paper/2404.17243/) (2024)
- [A Layer-Wise Tokens-to-Token Transformer Network for Improved Historical Document Image Enhancement](https://mlsanity.com/paper/2312.03946/) (2023)
- [Npix2Cpix: A GAN-Based Image-to-Image Translation Network With Retrieval- Classification Integration for Watermark Retrieval From Historical Document Images](https://mlsanity.com/paper/2406.03556/) (2024)
- [TextDoctor: Unified Document Image Inpainting via Patch Pyramid Diffusion Models](https://mlsanity.com/paper/2503.04021/) (2025)
- [MFE-GAN: Efficient GAN-based Framework for Document Image Enhancement and Binarization with Multi-scale Feature Extraction](https://mlsanity.com/paper/2512.14114/) (2025)
- [synthocr-gen: A synthetic ocr dataset generator for low-resource languages- breaking the data barrier](https://mlsanity.com/paper/2601.16113/) (2026)
- [Generative Adversarial Networks via a Composite Annealing of Noise and Diffusion](https://mlsanity.com/paper/2105.00220/) (2021)
- [Digitizing Historical Balance Sheet Data: A Practitioner's Guide](https://mlsanity.com/paper/2204.00052/) (2022)
- [DECDM: Document Enhancement using Cycle-Consistent Diffusion Models](https://mlsanity.com/paper/2311.09625/) (2023)

## Abstract (arXiv)

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.