Verification Code Recognition Based on Active and Deep Learning
This work addresses the challenge of automated verification code recognition for security applications, but it is incremental as it builds on existing convolutional neural network methods.
The study tackled the problem of recognizing verification codes with insufficient training data by proposing an active and deep learning strategy to automatically obtain new training data, which considerably improved recognition accuracy for neural networks in such scenarios.
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.