文献类型：期刊文献

英文题名：Fast arbitrary shaped scene text detection via text discriminator

作者：Zeng, Chengbin Song, Chunli

第一作者：曾成斌;Zeng, Chengbin

机构：[1] Guizhou Institute of Technology, Guiyzhou, Guiyang, China; [2] Guizhou Carefreesky Technology Co., Ltd., Guiyzhou, Guiyang, China

第一机构：贵州理工学院

年份：2021

卷号：2025

期号：1

外文期刊名：Journal of Physics: Conference Series

收录：EI(收录号：20214411090072)

语种：英文

外文关键词：Signal detection

摘要：Robust scene text detection is one of the difficult and significant challenges in the computer vision community. Most previous methods detect arbitrary-shaped text using complicated post-processing steps. In this paper, we propose a trainable fast arbitrary-shaped text detection network by using the text discriminator, sharing visual information among the two complementary tasks. Specifically, we extend PSENet [1] by adding a text discriminator to fuse multiple predictions for each text instance, rather than using complicated post-processing steps which are time consuming. The text discriminator shares visual information with text detection network, and thus can achieve much faster detection speed compared with PSENet, while maintaining a similar accuracy reported in PSENet. Furthermore, our text discriminator can reduce the false alarms effectively. Experiments on ICDAR 2017 MLT, ICDAR 2015, and ICDAR 2019 ART datasets demonstrate that the proposed approach can achieve nearly real-time detection speed while keeping state-of-the-art detection accuracy. ? Journal of Physics: Conference Series 2021.