Electronic Science and Technology ›› 2019, Vol. 32 ›› Issue (9): 42-46.doi: 10.16180/j.cnki.issn1007-7820.2019.09.009

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Point-selective Geetest CAPTCHA Recognition Based on Faster-RCNN

ZHOU Wenkai,HAN Fang,KONG Weijian   

  1. School of Information Science and Technology,Donghua University,Shanghai 201620,China
  • Received:2018-09-12 Online:2019-09-15 Published:2019-09-19
  • Supported by:
    National Natural Science Foundation of China(11572084);National Natural Science Foundation of China(11472061);The Fundamental Research Funds for the Central Universities(18D210402)


In view of the problem that traditional methods were difficult to recognize the geetest point-selective CAPTCHA , this paper proposed a recognition method that based on Faster-RCNN object detection model and convolutional neural network. Firstly, it improved the accuracy of text positioning by simplified RPN, then designed convolutional neural network to classify and identify texts,and trained the Tesseract-OCR identification databases to recognize the message texts. Finally, the result of texts recognition which embedded into background picture corresponded to the result of message texts recognition, then it reached the purpose of CAPTCHA recognition. Experimental results showed that the accuracy of this recognition method up to 72.4%.

Key words: geetest CAPTCHA recognition, Faster-RCNN, convolutional neural network, Tesseract-OCR

CLC Number: 

  • TP391