A Character Flow Framework for Multi-Oriented Scene Text Detection

A Character Flow Framework for Multi-Oriented Scene Text Detection
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一种面向多方向场景文本检测的字符流框架

DOI:
10.1007/s11390-021-1362-4
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发表时间:
2021-05
影响因子:
0.7
通讯作者:
Shu Liu
Shu Liu
中科院分区:
--
文献类型:
--
作者:
Wen-Jun Yang;Bei-Ji Zou;Kai-Wen Li;Shu Liu

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场景文本检测在各种应用中起着重要作用,例如对象识别,文档管理和视觉导航。基于实例细分的方法主要用于现有研究,因为其优势在Dealin中
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.
DOI: 10.1109/cvpr.2016.451
发表时间: 2016-04
期刊: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
影响因子: --
作者:
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发表时间: 2017-11-01
影响因子: 23.6
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