Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (2): 201-208.doi: 10.11896/jsjkx.211000191

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Scene Text Detection with Improved Region Proposal Network

LI Junlin1, OUYANG Zhi2, DU Nisuo1,2   

  1. 1 College of Computer Science and Technology,Guizhou University,Guiyang 550025,China
    2 Guizhou Big Data Academy,Guizhou University,Guiyang 550025,China
  • Received:2021-10-26 Revised:2022-03-18 Online:2023-02-15 Published:2023-02-22
  • Supported by:
    Major Scientific and Technological Special Project of Guizhou Province China([2018]3002) and Cultivation Project of Guizhou University([2020]-41)

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Abstract: Scene text images have very complex and changeable features.Using region proposal network(RPN) to extract text rectangle position candidate boxes is an indispensable step,which can greatly improve the accuracy of text detection.However,recent studies show that the methods of regressing the center point,width and height of the text rectangular candidate boxes by minimizing the smooth L1 loss function would easily cause problems such as missing boundary information and inaccurate regression.Therefore,this paper proposes a scene text detection model based on improved region proposal network.First,the backbone network composed of the residual network and the feature pyramid network is used to generate a shared feature map.Then,an improved regression method and vertex-based loss function(Vertex-IOU) are used to generate a series of text rectangular candidate boxes on the shared feature map.Finally,ROI Align is used to convert these candidate boxes into fixed-size feature maps for bounding box regression in the fully connected layer.Through comparative experiments on ICDAR2015 dataset,the results show that the test effect is improved compared with other models,which proves the effectiveness of our model.

Key words: Keywords deep learning, Scene text detection, Region proposal network, Regression method, Loss function

CLC Number: 

  • TP391
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