CAPTCHA recognition based on deep convolutional neural network

Jing Wang; Jiaohua Qin; Xuyu Xiang; Yun Tan; Nan Pan; Jing Wang; Jiaohua Qin; Xuyu Xiang; Yun Tan; Nan Pan

doi:10.3934/mbe.2019292

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 5: 5851-5861. doi: 10.3934/mbe.2019292

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Research article Special Issues

CAPTCHA recognition based on deep convolutional neural network

College of Computer Science and Information Technology, Central South University of Forestry and Technology, 498 shaoshan S Rd, Changsha, 410004, China

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%.
- CAPTCHA recognition,
- deep learning,
- convolutional neural network,
- DenseNet,
- ResNet
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

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Abstract

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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