SiameseCCR: a novel method for one‐shot and few‐shot Chinese CAPTCHA recognition using deep Siamese network

Abstract

The research of CAPTCHA recognition is helpful to discover the security vulnerabilities in time and improve its safety. In comparison with digits and English letters, Chinese characters have many more categories which lead to the requirement of a large amount of training data. Therefore, this study proposes a novel method for one-shot and few-shot Chinese CAPTCHA recognition, using the deep Siamese network, based on the idea of template matching. In this method, the residual convolutional neural network branches are used for feature extraction of CAPTCHAs, a fully-connected layer is used for calculating the similarity of features, and a hard negative mining algorithm is designed to promote convergence. Experiments are done on a self-built small-scale Chinese CAPTCHA dataset. The results show that this proposed method can achieve higher accuracy on the known characters than traditional methods. For the brand-new characters, only one template is required to recognise them and the accuracy is close to known characters. To summarise, it is able to build a Chinese CAPTCHA recognition model with high accuracy and extensibility by using a small-scale dataset.