  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.
