Image CAPTCHA Recognition Based on Convolutional Neural Network

doi:10.15888/j.cnki.csa.006648

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Home > Archive>Volume 27, Issue 11, 2018 >142-148. DOI:10.15888/j.cnki.csa.006648

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Image CAPTCHA Recognition Based on Convolutional Neural Network
DOI:
                        10.15888/j.cnki.csa.006648
                    
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                        QIN BoQIN Bo
Laboratory of Network Computing and High Efficient Algorithm, School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China;
Anhui Provincial Key Laboratory of Computing and Communication Software, Hefei 230027, China;
Institute of Advanced Technology, University of Science and Technology of China, Hefei 230027, China
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GU Nai-JieGU Nai-Jie
Laboratory of Network Computing and High Efficient Algorithm, School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China;
Anhui Provincial Key Laboratory of Computing and Communication Software, Hefei 230027, China;
Institute of Advanced Technology, University of Science and Technology of China, Hefei 230027, China
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ZHANG Xiao-CiZHANG Xiao-Ci
Laboratory of Network Computing and High Efficient Algorithm, School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China;
Anhui Provincial Key Laboratory of Computing and Communication Software, Hefei 230027, China;
Institute of Advanced Technology, University of Science and Technology of China, Hefei 230027, China
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LIN Chuan-WenLIN Chuan-Wen
Laboratory of Network Computing and High Efficient Algorithm, School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China;
Anhui Provincial Key Laboratory of Computing and Communication Software, Hefei 230027, China;
Institute of Advanced Technology, University of Science and Technology of China, Hefei 230027, China
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Abstract:

As a security measure, CAPTCHA is widely used in Internet. This study proposes a CAPTCHA identification method based on convolutional neural network. Through convolutional layer concatenation, residual learning, global pool, and other technical means, under the premise of ensuring the recognition accuracy rate is not affected, it greatly reduces the amount of network parameters. This study uses the CAPTCHA in the railway ticket website and the CAPTCHA in the educational system as examples to test the performance of the model. For the CAPTCHA in railway ticket website, the experimental results show that this method has the least amount of parameters, and the recognition accuracy of this method is 98.76% for image and the recognition accuracy of the Chinese phrases is 99.14%. For the CAPTCHA in educational system, it has the least amount of parameters and the accuracy is 87.30%.

Key words:image CAPTCHA recognition;convolutional neural network;residual learning;visualization

Reference

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秦波,顾乃杰,张孝慈,林传文.基于卷积神经网络的图像验证码识别.计算机系统应用,2018,27(11):142-148

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Received:April 02,2018
Revised:May 11,2018
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Online: October 24,2018
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