GDB: Gated convolutions-based Document Binarization

TL;DR

This paper introduces GDB, a gated convolutions-based neural network for document binarization that enhances stroke edge extraction by selectively focusing on relevant features, outperforming existing methods across multiple datasets.

Contribution

The paper proposes a novel end-to-end gated convolutions framework with a two-stage process and multi-scale features for improved stroke edge extraction in document binarization.

Findings

Outperforms state-of-the-art methods on ten DIBCO datasets.

Achieves top ranking on six benchmark datasets.

Effectively extracts fine stroke edges with gating mechanisms.

Abstract

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.