arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1902.04401cs.CRcs.LG

Verification Code Recognition Based on Active and Deep Learning

Shandong University(山东大学);Harbin Institute of Technology(哈尔滨工业大学);Temple University(天普大学);Xidian University(西安电子科技大学);North China Electric Power University(华北电力大学);Capital Normal University(首都师范大学);Intermediate People's Court(中级人民法院)

Dongliang Xu, Bailing Wang, XiaoJiang Du, Xiaoyan Zhu, zhitao Guan, Xiaoyan Yu, Jingyu Liu

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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