# CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization

arXiv: https://arxiv.org/abs/2305.17420 · HTML: https://mlsanity.com/paper/2305.17420/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 27 May 2023.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Document Image Binarization with Fully Convolutional Neural Networks](https://mlsanity.com/paper/1708.03276/) (2017)
- [Improving Document Binarization via Adversarial Noise-Texture Augmentation](https://mlsanity.com/paper/1810.11120/) (2018)
- [GDB: Gated convolutions-based Document Binarization](https://mlsanity.com/paper/2302.02073/) (2023)
- [Two-stage generative adversarial networks for document image binarization with color noise and background removal](https://mlsanity.com/paper/2010.10103/) (2020)
- [Splicing Detection and Localization In Satellite Imagery Using Conditional GANs](https://mlsanity.com/paper/2205.01805/) (2022)

## Built on by (most cited first, 2 of 2)

- [DKDS: A Benchmark Dataset of Degraded Kuzushiji Documents with Seals for Detection and Binarization](https://mlsanity.com/paper/2511.09117/) (2025)
- [Integrated Framework for Selecting and Enhancing Ancient Marathi Inscription Images from Stone, Metal Plate, and Paper Documents](https://mlsanity.com/paper/2601.04800/) (2026)

## Abstract (arXiv)

To efficiently extract textual information from color degraded document images is a significant research area. The prolonged imperfect preservation of ancient documents has led to various types of degradation, such as page staining, paper yellowing, and ink bleeding. These types of degradation badly impact the image processing for features extraction. This paper introduces a novelty method employing generative adversarial networks based on color channel using discrete wavelet transform (CCDWT-GAN). The proposed method involves three stages: image preprocessing, image enhancement, and image binarization. In the initial step, we apply discrete wavelet transform (DWT) to retain the low-low (LL) subband image, thereby enhancing image quality. Subsequently, we divide the original input image into four single-channel colors (red, green, blue, and gray) to separately train adversarial networks. For the extraction of global and local features, we utilize the output image from the image enhancement stage and the entire input image to train adversarial networks independently, and then combine these two results as the final output. To validate the positive impact of the image enhancement and binarization stages on model performance, we conduct an ablation study. This work compares the performance of the proposed method with other state-of-the-art (SOTA) methods on DIBCO and H-DIBCO ((Handwritten) Document Image Binarization Competition) datasets. The experimental results demonstrate that CCDWT-GAN achieves a top two performance on multiple benchmark datasets. Notably, on DIBCO 2013 and 2016 dataset, our method achieves F-measure (FM) values of 95.24 and 91.46, respectively.