Verification Code Recognition Based on Active and Deep Learning

Dongliang Xu, Bailing Wang, Xiao Jiang Du, Xiaoyan Zhu, Zhitao Guan, Xiaoyan Yu, Jingyu Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2019 International Conference on Computing, Networking and Communications, ICNC 2019
Pages453-456
Number of pages4
ISBN (Electronic)9781538692233
DOIs
StatePublished - 8 Apr 2019
Event2019 International Conference on Computing, Networking and Communications, ICNC 2019 - Honolulu, United States
Duration: 18 Feb 201921 Feb 2019

Publication series

Name2019 International Conference on Computing, Networking and Communications, ICNC 2019

Conference

Conference2019 International Conference on Computing, Networking and Communications, ICNC 2019
Country/TerritoryUnited States
CityHonolulu
Period18/02/1921/02/19

Keywords

  • Verification code recognition
  • convolutional neural network
  • feature learning

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