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Document binarization is a key pre-processing step for many document analysistasks. However, existing methods can not extract stroke edges finely, mainlydue to the fair-treatment nature of vanilla convolutions and the extraction ofstroke edges without adequate supervision by boundary-related information. Inthis paper, we formulate text extraction as the learning of gating values andpropose an end-to-end gated convolutions-based network (GDB) to solve theproblem of imprecise stroke edge extraction. The gated convolutions are appliedto selectively extract the features of strokes with different attention. Ourproposed framework consists of two stages. Firstly, a coarse sub-network withan extra edge branch is trained to get more precise feature maps by feeding apriori mask and edge. Secondly, a refinement sub-network is cascaded to refinethe output of the first stage by gated convolutions based on the sharp edge.For global information, GDB also contains a multi-scale operation to combinelocal and global features. We conduct comprehensive experiments on ten DocumentImage Binarization Contest (DIBCO) datasets from 2009 to 2019. Experimentalresults show that our proposed methods outperform the state-of-the-art methodsin terms of all metrics on average and achieve top ranking on six benchmarkdatasets.

GDB: Gated convolutions-based Document Binarization