An Accurate Segmentation-Based Scene Text Detector with Context Attention and Repulsive Text Border

Abstract

Scene text detection is one of the most challenging problems in computer vision and has attracted great interest. In general, scene text detection methods are divided into two categories: detection-based and segmentation-based methods. Recently, the segmentation-based methods are more and more popular due to their superior performances and the advantages of detecting arbitrary-shape texts. However, there still exist the following problems: (a) the misclassfication of the unexpected texts, (b) the split of long text lines, (c) the failure of separating very close text instances. In this paper, we propose an accurate segmentation-based detector, which is equipped with context attention and repulsive text border. The context attention incorporates global channel attention, non-local self-attention and spatial attention to better exploit the global and local context, which can greatly increase the discriminative ability for pixels. Due to the enhancement of pixel-level features, false positives and the misdetections of long texts are reduced. Besides, for the purpose of solving very close text instance, a repulsive pixel link, which focuses on the relationships between pixels at the border, is proposed. Experiments on several standard benchmarks, including MSRA-TD500, ICDAR2015, ICDAR2017-MLT and CTW1500, validate the superiority of the proposed method.