The efficient segmentation of foreground text information from the background
in degraded color document images is a hot research topic. Due to the imperfect
preservation of ancient documents over a long period of time, various types of
degradation, including staining, yellowing, and ink seepage, have seriously
affected the results of image binarization. In this paper, a three-stage method
is proposed for image enhancement and binarization of degraded color document
images by using discrete wavelet transform (DWT) and generative adversarial
network (GAN). In Stage-1, we use DWT and retain the LL subband images to
achieve the image enhancement. In Stage-2, the original input image is split
into four (Red, Green, Blue and Gray) single-channel images, each of which
trains the independent adversarial networks. The trained adversarial network
models are used to extract the color foreground information from the images. In
Stage-3, in order to combine global and local features, the output image from
Stage-2 and the original input image are used to train the independent
adversarial networks for document binarization. The experimental results
demonstrate that our proposed method outperforms many classical and
state-of-the-art (SOTA) methods on the Document Image Binarization Contest
(DIBCO) dataset. We release our implementation code at
https://github.com/abcpp12383/ThreeStageBinarization