  Degraded document image binarization is one of the most challenging tasks in
the domain of document image analysis. In this paper, we present a novel
approach towards document image binarization by introducing three-player
min-max adversarial game. We train the network in an unsupervised setup by
assuming that we do not have any paired-training data. In our approach, an
Adversarial Texture Augmentation Network (ATANet) first superimposes the
texture of a degraded reference image over a clean image. Later, the clean
image along with its generated degraded version constitute the pseudo
paired-data which is used to train the Unsupervised Document Binarization
Network (UDBNet). Following this approach, we have enlarged the document
binarization datasets as it generates multiple images having same content
feature but different textual feature. These generated noisy images are then
fed into the UDBNet to get back the clean version. The joint discriminator
which is the third-player of our three-player min-max adversarial game tries to
couple both the ATANet and UDBNet. The three-player min-max adversarial game
stops, when the distributions modelled by the ATANet and the UDBNet align to
the same joint distribution over time. Thus, the joint discriminator enforces
the UDBNet to perform better on real degraded image. The experimental results
indicate the superior performance of the proposed model over existing
state-of-the-art algorithm on widely used DIBCO datasets. The source code of
the proposed system is publicly available at
https://github.com/VIROBO-15/UDBNET.
