关键词: 验证码, 汉字验证码, CNN, Keras框架

Abstract: CAPTCHAs（Completed Automated Public Turing test to tell Computers and Humans Apart） have already been widely applied in various fields of social life. Automatic recognition of CAPTCHAs consisting of English letters and Arabic numerals has already reached an advanced level. While with general methods identifing the CAPTCHAs?consisting of Chinese characters seems too difficult and the accuracy needs to be promoted. This paper mainly proposes a method of automatic identification CAPTCHAs which is based on convolutional neural network to improve the accuracy of characters recognition. In order to improve the generalization performance of the model by which adopting the framework of Keras convolution neural network and designing of multilayer convolution to extract deep-layer image information of which identifing Chinese characters CAPTCHAs and alphanumeric CAPTCHAs respectively. The experimental results indicate that the accuracy of identification has been promoted remarkably. The identification rate of Chinese characters is up to 99.4%. Meanwhile, the maximum of the identification rate of alphanumeric four-character CAPTCHAs is as high as 99.3%. These findings show that the Deep Neural Network possesses an excellent perceptivity against complex structures. It can be seen from the comparative experiments that the framework of Keras convolution neural network has better performance than other frameworks in CAPTCHAs recognition.

Key words: CAPTCHAs（Completed Automated Public Turing test to tell Computers and Humans Apart）, Chinese character CAPTCHAs, CNN, Keras framework