Leveraging the Mixed-Text Segmentation Problem to Design Secure Handwritten CAPTCHAs

Abstract

In this paper we present a novel CAPTCHA that is based on the current hard AI problem of mixed-text (handwriting and printed-text) segmentation. The proposed CAPTCHA overlays generated handwritten word images on a generated printed-text background. We first propose a modification that allows for character level perturbations on an existing synthetic handwriting generation technique. These perturbations are parameterized allowing for varying levels of handwritten word complexity. We then use the output from the modified synthetic handwriting generator as the foreground for the mixed-text CAPTCHA. Experiments show that the proposed approach is effective at successfully distinguishing between humans and machines. Human recognition accuracy averages at 0.77 while machine accuracy is below 0.0001.