  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.
