A sigma-lognormal model for character level CAPTCHA generation

Word level handwritten CAPTCHA generation involves picking a handwritten word from a pre-existing database and cumulatively applying distortions and noise models. In principle, the addition of distortion and noise makes the CAPTCHA robust to automated attacks. However, the primary drawback of the word level CAPTCHA generation is that it limits us to words that already exist in our data set. If the primary building block of this approach was a character, we could move away from a lexicon based CAPTCHA generation and generate CAPTCHAs which are resistant to a dictionary based attack. In this paper, we propose a Sigma-Lognormal based approach to generate character level CAPTCHAs. Next, we increase the robustness of the model by applying ideas from accents in handwriting to our problem. Finally, we demonstrate the efficacy of our approach by simulating an attack by an automated word recognizer.