Distortion estimation techniques in solving visual CAPTCHAs

This paper describes two distortion estimation techniques for object recognition that solve EZ-Gimpy and Gimpy-r, two of the visual CAPTCHAs ("completely automated public turing test to tell computers and humans apart") with high degrees of success. A CAPTCHA is a program that generates and grades tests that most humans can pass but current computer programs cannot pass. We have developed a correlation algorithm that correctly identifies the word in an EZ-Gimpy challenge image 99% of the time and a direct distortion estimation algorithm that correctly identifies the four letters in a Gimpy-r challenge image 78% of the time.