The efficient segmentation of foreground text information from the background in degraded color document images is a critical challenge in the preservation of ancient manuscripts. The imperfect preservation of ancient manuscripts over time has led to various types of degradation, such as staining, yellowing, and ink seepage, significantly affecting image binarization results. This work proposes a three-stage method using Generative Adversarial Networks (GAN) for enhancing and binarizing degraded color document images through Discrete Wavelet Transform (DWT). Stage-1 involves applying DWT and retaining the Low-Low (LL) subband images for image enhancement. In Stage-2, the original input image is divided into four single-channel images (Red, Green, Blue, and Gray), and each is trained with independent adversarial networks to extract color foreground information. In Stage-3, the output image from Stage-2 and the original input image are used to train independent adversarial networks for document binarization, enabling the integration of global and local features. The experimental results demonstrate that our proposed method outperforms other classic and state-of-the-art (SOTA) methods on the Document Image Binarization Contest (DIBCO) datasets. We have released our implementation code at https://github.com/abcpp12383/ThreeStageBinarization.

翻译：对退化彩色文档图像中前景文本信息与背景的高效分割是古代手稿保存中的关键挑战。古代手稿因保存不善随时间推移产生多种退化类型，如污渍、泛黄及墨迹渗透，显著影响图像二值化效果。本文提出一种三阶段方法，利用生成对抗网络(GAN)通过离散小波变换(DWT)对退化彩色文档图像进行增强与二值化。第一阶段应用DWT并保留低频子带图像进行图像增强。第二阶段将原始输入图像分解为红、绿、蓝及灰度四个单通道图像，分别用独立对抗网络训练以提取彩色前景信息。第三阶段使用第二阶段输出图像与原始输入图像训练独立对抗网络实现文档二值化，从而融合全局与局部特征。实验结果表明，在文档图像二值化竞赛(DIBCO)数据集上，本文方法优于其他经典及最新方法。我们已在https://github.com/abcpp12383/ThreeStageBinarization 公开实现代码。