A Character Flow Framework for Multi-Oriented Scene Text Detection

• 作者: Wen-Jun Yang Bei-Ji Zou Kai-Wen Li Shu Liu
• 来源期刊: 计算机科学技术学报（英文版）
• 年，卷（期）: 2021,（3）
• 所属期刊栏目: Special Section of CVM 2021
• 页码范围: 465-477
• 页数: 13页
• 语种: 英文

摘要

Scene text detection plays a significant role in various applications,such as object recognition,document management,and visual navigation.The instance segmentation based method has been mostly used in existing research due to its advantages in dealing with multi-oriented texts.However,a large number of non-text pixels exist in the labels during the model training,leading to text mis-segmentation.In this paper,we propose a novel multi-oriented scene text detection framework,which includes two main modules:character instance segmentation (one instance corresponds to one character),and character flow construction (one character flow corresponds to one word).We use feature pyramid network(FPN) to predict character and non-character instances with arbitrary directions.A joint network of FPN and bidirectional long short-term memory (BLSTM) is developed to explore the context information among isolated characters,which are finally grouped into character flows.Extensive experiments are conducted on ICDAR2013,ICDAR2015,MSRA-TD500 and MLT datasets to demonstrate the effectiveness of our approach.The F-measures are 92.62％,88.02％,83.69％ and 77.81％,respectively.