Improving Document Binarization Via Adversarial Noise-Texture Augmentation
Binarization of degraded document images is an elementary step in most problems involving document image analysis. The paper re-visits the binarization problem by introducing an adversarial learning approach. We construct a Texture Augmentation Network that transfers the texture element of a degraded reference document image to a clean binary image. In this way, the network creates multiple versions of the same textual content with various noisy textures, thus enlarging the available document binarization datasets. Finally, the newly generated images are passed through a Binarization network to get back the clean version. By jointly training the two networks we can increase the adversarial robustness of our system. The most significant contribution of our framework is that it does not require any paired data unlike other Deep Learning-based methods [1], [2], [3]. Such a novel approach has never been implemented earlier thus making it the very first of its kind in Document Image Analysis community. Experimental results suggest that the proposed method1 achieves superior performance over widely used DIBCO datasets.