Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (2): 248-255.doi: 10.11896/jsjkx.201100072

• Artificial Intelligence • Previous Articles     Next Articles

Scene Text Detection Algorithm Based on Enhanced Feature Pyramid Network

SHAO Hai-lin1, JI Yi1, LIU Chun-ping1, XU Yun-long2   

  1. 1 School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China
    2 Applied Technology College of Soochow University,Suzhou,Jiangsu 215300,China
  • Received:2020-11-09 Revised:2021-05-02 Online:2022-02-15 Published:2022-02-23
  • About author:SHAO Hai-lin,born in 1995,postgra-duate,is a member of China Computer Federation.Her main research interests include scene text detection and so on.
    LIU Chun-ping,born in 1971,Ph.D,professor,Ph.D supervisor.Her main research interests include computer vision,image analysis and recognition,in particular in domains of visual saliency detection,objection and scene understanding.
  • Supported by:
    National Natural Science Foundation of China(61972059,61773272,61602332),Natural Science Foundation of Jiangsu Higher Education Institutions of China(19KJA230001),Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University(93K172016K08) and Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Abstract: Scene text detection helps machines understand image content,and is widely used in the fields such as intelligent transportation,scene understanding,and intelligent navigation.Existing scene text detection algorithms do not make full use of high-level semantic information and spatial information,which limits the model's ability to classify complex background pixels and the ability to detect and locate text instances of different scales.In order to solve the above problems,a scene text detection algorithm based on enhanced feature pyramid network is proposed.The algorithm includes a RIFE (ratio invariant feature enhanced) mo-dule and a RSR (rebuild spatial resolution) module.As the residual branch,the RIFE module enhances the high-level semantic information transmission of the network,improves the classification ability,and reduces the false positive rate and the false negative rate.The RSR module reconstructs multi-layer feature resolution and uses rich spatial information to improve the boundary location.Experimental results show that the proposed algorithm improves the detection capabilities on the multi-directional text dataset ICDAR2015,the curved text dataset Totaltext,and the long text dataset MSRA-TD500.

Key words: Boundary location, Feature pyramid network, Scene text detection, Semantic information, Spatial information

CLC Number: 

  • TP391
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