Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6): 256-263.doi: 10.11896/jsjkx.230500230

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Scene Text Detection Algorithm Based on Feature Enhancement

GAO Nan, ZHANG Lei, LIANG Ronghua, CHEN Peng, FU Zheng   

  1. College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
  • Received:2023-05-31 Revised:2023-10-25 Online:2024-06-15 Published:2024-06-05
  • About author:GAO Nan,Ph.D,born in 1983,is a member of CCF(No.83932F).Her main research interests include cross modal generation and retrieval,natural language processing,medical image processing,etc.
  • Supported by:
    National Natural Science Foundation of China(61702456,62036009,U1909203) and National Key Research and Development Program of China(2020YFB1707700).

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Abstract: To address the problem of missed and false detection of image text in natural scenes due to complex backgrounds and variable scales,this paper proposes a text detection algorithm for scenes based on feature enhancement.In the feature pyramid fusion stage,a dual-domain attention feature fusion module(D2AAFM)is proposed,which can better fuse feature map information of different semantics and scales,thus improving the characterization ability of text information.At the same time,considering the problem of semantic information loss in the process of up-sampling and fusion of deeper feature maps of the network,the multi-scale spatial perception module(MSPM)is proposed to enhance the semantic features of text in higher-level feature maps by expanding the perceptual field to obtain contextual information of a larger perceptual field,thus effectively reduce the text of missed and false detection.In order to evaluate the effectiveness of the proposed algorithm,it is tested on the publicly available datasets ICDAR2015,CTW1500 and MSRA-TD500,and its overall index F-value reaches 82.8%,83.4% and 85.3%,respectively.The experimental results show that the algorithm has good detection capability on different datasets.

Key words: Deep learning, Scene text detection, Attention mechanisms, Multi-scale feature fusion, Dilated convolution

CLC Number: 

  • TP391
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