  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/.
