| Title | End-to-End Captcha Recognition Using Deep CNN-RNN Network |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Shu, Yujin, Xu, Yongjin |
| Conference Name | 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) |
| Keywords | 4-character text captcha, Backpropagation, CAPTCHA, CAPTCHA recognition, captchas, CNN-RNN, CNN-RNN network model, completely automated public turing test, composability, convolutional neural nets, deep residual convolutional neural network, end-to-end captcha recognition, Gated Recurrent Unit, Human Behavior, human computer interaction, image recognition, input captcha picture features, Internet, pubcrawl, recurrent neural nets, Residual, security of data, two-layer GRU network |
| Abstract | With the development of the Internet, the captcha technology has also been widely used. Captcha technology is used to distinguish between humans and machines, namely Completely Automated Public Turing test to tell Computers and Humans Apart. In this paper, an end-to-end deep CNN-RNN network model is constructed by studying the captcha recognition technology, which realizes the recognition of 4-character text captcha. The CNN-RNN model first constructs a deep residual convolutional neural network based on the residual network structure to accurately extract the input captcha picture features. Then, through the constructed variant RNN network, that is, the two-layer GRU network, the deep internal features of the captcha are extracted, and finally, the output sequence is the 4-character captcha. The experiments results show that the end-to-end deep CNN-RNN network model has a good performance on different captcha datasets, achieving 99% accuracy. And experiment on the few samples dataset which only has 4000 training samples also shows an accuracy of 72.9 % and a certain generalization ability. |
| DOI | 10.1109/IMCEC46724.2019.8983895 |
| Citation Key | shu_end–end_2019 |
End-to-End Captcha Recognition Using Deep CNN-RNN Network