标题:Verification Code Recognition Based on Active and Deep Learning
作者:Xu, Dongliang; Wang, Bailing; Du, XiaoJiang; Zhu, Xiaoyan; Guan, Zhitao; Yu, Xiaoyan; Liu, Jingyu
通讯作者:Xu, DL
作者机构:[Xu, Dongliang] Shandong Univ, Sch Comp Sci & Technol, Weihai, Peoples R China.; [Wang, Bailing] Harbin Inst Technol, Sch Comp Sci & Technol, Weihai 更多
会议名称:International Conference on Computing, Networking and Communications (ICNC)
会议日期:FEB 18-21, 2019
来源:2019 INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKING AND COMMUNICATIONS (ICNC)
出版年:2019
页码:453-456
关键词:Verification code recognition; convolutional neural network; feature; learning
摘要:A verification code is an automated test method used to distinguish between humans and computers. Humans can easily identify verification codes, whereas machines cannot. With the development of convolutional neural networks, automatically recognizing a verification code is now possible for machines. However, the advantages of convolutional neural networks depend on the data used by the training classifier, particularly the size of the training set. Therefore, identifying a verification code using a convolutional neural network is difficult when training data are insufficient. This study proposes an active and deep learning strategy to obtain new training data on a special verification code set without manual intervention. A feature learning model for a scene with less training data is presented in this work, and the verification code is identified by the designed convolutional neural network. Experiments show that the method can considerably improve the recognition accuracy of a neural network when the amount of initial training data is small.
收录类别:CPCI-S
资源类型:会议论文
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