# Two-stage generative adversarial networks for document image binarization with color noise and background removal

arXiv: https://arxiv.org/abs/2010.10103 · HTML: https://mlsanity.com/paper/2010.10103/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 8 of 8)

- [Three-stage binarization of color document images based on discrete wavelet transform and generative adversarial networks](https://mlsanity.com/paper/2211.16098/) (2022)
- [Binarizing Documents by Leveraging both Space and Frequency](https://mlsanity.com/paper/2404.17243/) (2024)
- [Synthesis in Style: Semantic Segmentation of Historical Documents using Synthetic Data](https://mlsanity.com/paper/2107.06777/) (2021)
- [CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization](https://mlsanity.com/paper/2305.17420/) (2023)
- [A Layer-Wise Tokens-to-Token Transformer Network for Improved Historical Document Image Enhancement](https://mlsanity.com/paper/2312.03946/) (2023)
- [A Fair Evaluation of Various Deep Learning-Based Document Image Binarization Approaches](https://mlsanity.com/paper/2401.11831/) (2024)
- [Document Image Binarization in JPEG Compressed Domain using Dual Discriminator Generative Adversarial Networks](https://mlsanity.com/paper/2209.05921/) (2022)
- [Variational Augmentation for Enhancing Historical Document Image Binarization](https://mlsanity.com/paper/2211.06581/) (2022)

## Abstract (arXiv)

Document image enhancement and binarization methods are often used to improve the accuracy and efficiency of document image analysis tasks such as text recognition. Traditional non-machine-learning methods are constructed on low-level features in an unsupervised manner but have difficulty with binarization on documents with severely degraded backgrounds. Convolutional neural network-based methods focus only on grayscale images and on local textual features. In this paper, we propose a two-stage color document image enhancement and binarization method using generative adversarial neural networks. In the first stage, four color-independent adversarial networks are trained to extract color foreground information from an input image for document image enhancement. In the second stage, two independent adversarial networks with global and local features are trained for image binarization of documents of variable size. For the adversarial neural networks, we formulate loss functions between a discriminator and generators having an encoder-decoder structure. Experimental results show that the proposed method achieves better performance than many classical and state-of-the-art algorithms over the Document Image Binarization Contest (DIBCO) datasets, the LRDE Document Binarization Dataset (LRDE DBD), and our shipping label image dataset. We plan to release the shipping label dataset as well as our implementation code at github.com/opensuh/DocumentBinarization/.