Methods › Computer Vision › Generative Adversarial Networks › DE-GAN
DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement
DE-GAN
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
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.
Papers archive 2025-07-28
4 shown of 4, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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A Fair Evaluation of Various Deep Learning-Based Document Image Binarization Approaches 22 Jan 2024 · 1 repository · arXiv:2401.11831
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DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement 17 Oct 2020 · 4 repositories · arXiv:2010.08764
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Facial Expression Representation Learning by Synthesizing Expression Images 30 Nov 2019 · 0 repositories · arXiv:1912.01456
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Facial Expression Recognition Using Disentangled Adversarial Learning 28 Sep 2019 · 0 repositories · arXiv:1909.13135
Tasks archive 2025-07-28
12 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Generative Adversarial Network | 3 |
| Binarization | 2 |
| Decoder | 2 |
| Facial Expression Recognition | 2 |
| Facial Expression Recognition (FER) | 2 |
| Representation Learning | 2 |
| Deblurring | 1 |
| Deep Learning | 1 |
| Document Enhancement | 1 |
| Image Reconstruction | 1 |
| Optical Character Recognition (OCR) | 1 |
| valid | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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