Margin Guidance Network for Arbitrary-shaped Scene Text Detection

Abstract

Segmentation-based scene text detection approaches have been adopted to arbitrary-shaped texts and have achieved a great progress. However, false detection always easily exist when the arbitrary-shaped texts are close to each other. In this paper, we propose the Margin Guidance Network (MGN) that mainly based on the margin constraint residual module (MCRM) to address aforementioned problem. The MCRM considers the margins between multiple text instance masks to guide the training of network and improve the performance on text detection. The MCRM contains two prediction branch, the one can generate the multiple different scale of masks for a text instance and the other branch is used to generate multiple margins between the above masks. Experimental results on three public benchmarks including ICDAR2015, CTW1500 and Total-Text have demonstrated that the proposed MGN achieves the state-of-the-art results.