Document binarization with Multi-branch Gated Convolutional Generative Adversarial Networks

Existing document binarization methods can not extract stroke edges finely, mainly due to the fair-treatment nature of vanilla convolutions and the extraction of stroke edges without adequate supervision by boundary-related information. In this paper, we formulate text extraction as the learning of gating values and propose a novel end-to-end gated convolutions-based network (GDB) to solve the problem of imprecise stroke edge extraction. The gated convolutions are applied to selectively extract the features of strokes with different attention. Firstly, a coarse sub-network with an extra edge branch is trained to get more precise feature maps by feeding a priori mask and edge. Secondly, a refinement sub-network is cascaded to refine the output of the first stage by gated convolutions based on the sharp edge. For global information, GDB also contains a multi-scale operation to combine local and global features. Experimental results show that our proposed methods outperform the SOTA methods in terms of all metrics on average over all DIBCO datasets from 2009 to 2019 and achieve top ranking on six benchmark datasets. Available codes: https://github.com/Royalvice/GDB.