A Multi-Label Neural Network Approach to Solving Connected CAPTCHAs

Text-based CAPTCHA as a security technology is used widely to distinguish human beings from computer programs. Compared with the classification of sub-image containing individual character, segmentation is the key to standard approaches to solving CAPTCHAs automatically. However, the effectiveness of the traditional approaches is limited when the characters in CAPTCHAs are connected and distorted. In this paper, we propose a novel approach to solving CAPTCHAs without segmentation via using a multi-label convolutional neural network. The design of the network refers to the procedure that humans recognize CAPTCHAs containing connected characters and learn the correlation between neighboring characters. Our approach archives high accuracy on various datasets of CAPTCHAs with sophisticated distortion and segmentation-resistance.