Research article Special Issues

CAPTCHA recognition based on deep convolutional neural network

  • Received: 05 January 2019 Accepted: 10 June 2019 Published: 24 June 2019
  • Aiming at the problems of low efficiency and poor accuracy of traditional CAPTCHA recognition methods, we have proposed a more efficient way based on deep convolutional neural network (CNN). The Dense Convolutional Network (DenseNet) has shown excellent classification performance which adopts cross-layer connection. Not only it effectively alleviates the vanishing-gradient problem, but also dramatically reduce the number of parameters. However, it also has caused great memory consumption. So we improve and construct a new DenseNet for CAPTCHA recognition (DFCR). Firstly, we reduce the number of convolutional blocks and build corresponding classifiers for different types of CAPTCHA images. Secondly, we input the CAPTCHA images of TFrecords format into the DFCR for model training. Finally, we test the Chinese or English CAPTCHAs experimentally with different numbers of characters. Experiments show that the new network not only keeps the primary performance advantages of the DenseNets but also effectively reduces the memory consumption. Furthermore, the recognition accuracy of CAPTCHA with the background noise and character adhesion is above 99.9%.

    Citation: Jing Wang, Jiaohua Qin, Xuyu Xiang, Yun Tan, Nan Pan. CAPTCHA recognition based on deep convolutional neural network[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 5851-5861. doi: 10.3934/mbe.2019292

    Related Papers:

  • Aiming at the problems of low efficiency and poor accuracy of traditional CAPTCHA recognition methods, we have proposed a more efficient way based on deep convolutional neural network (CNN). The Dense Convolutional Network (DenseNet) has shown excellent classification performance which adopts cross-layer connection. Not only it effectively alleviates the vanishing-gradient problem, but also dramatically reduce the number of parameters. However, it also has caused great memory consumption. So we improve and construct a new DenseNet for CAPTCHA recognition (DFCR). Firstly, we reduce the number of convolutional blocks and build corresponding classifiers for different types of CAPTCHA images. Secondly, we input the CAPTCHA images of TFrecords format into the DFCR for model training. Finally, we test the Chinese or English CAPTCHAs experimentally with different numbers of characters. Experiments show that the new network not only keeps the primary performance advantages of the DenseNets but also effectively reduces the memory consumption. Furthermore, the recognition accuracy of CAPTCHA with the background noise and character adhesion is above 99.9%.


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  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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