Pixel depth approach to deformed CAPTCHA skeleton

CAPTCHA is a standard security technology that presents test to tell computers and humans apart. Nowadays the most widely deployed CAPTCHAs are text-based schemes, which rely on sophisticated distortion of text images aimed at rendering them unrecognizable to the state of the art of pattern recognition methods. Generally, the skeletonization of character is acknowledged as one of the most significant parts in character recognition. The skeleton which keeps the topology information as well as reduces the computational complexity is an excellent and robust structural feature to noise and deformation. In this paper, a depth-based approach is proposed in order to locate the skeleton point. In order to strike the balance between efficiency and robustness against distortion, three fault tolerance techniques have been applied in the extraction process. Then in the amendment stage, we use noise patterns to filter redundant points. Experiments are conducted and positive results are achieved, which shows that the depth-based skeletonization scheme is applicable to the widely used CAPTCHA images, and the skeleton is robust against rotated, distorted or conglutinated characters.