  Document binarization is a key pre-processing step for many document analysis
tasks. However, existing 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 an 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. Our
proposed framework consists of two stages. 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. We conduct comprehensive experiments on ten Document
Image Binarization Contest (DIBCO) datasets from 2009 to 2019. Experimental
results show that our proposed methods outperform the state-of-the-art methods
in terms of all metrics on average and achieve top ranking on six benchmark
datasets.
