Exploiting the Gap in Human and Machine Abilities in Handwriting Recognition for Web Security Applications

Automated recognition of unconstrained handwriting continues to be a challenging research task. In contrast to the traditional role of handwriting recognition in applications such as postal automation, bank check reading etc, in this dissertation we explore the use of handwriting recognition for cyber security. HIPs (Human Interactive Proofs) are automatic reverse Turing tests designed so that virtually all humans can pass the test but state-of-theart computer programs will fail. Machine-printed, text-based HIPs are now commonly used to defend against bot attacks. We have designed a new methodology that will exploit the gap between the abilities of humans and computers in reading handwritten text images to design efficient HIPs. We have: (i) developed an algorithm to automatically generate random and infinitely many distinct handwritten HIPs, (ii) identified the weaknesses of state-of-the-art handwriting recognizers, and (iii) developed a method which exploits the strengths of human reading abilities that can be controlled, so that the HIPs are human readable but not machine readable. We have used a large repository of handwritten word images that current handwriting recognizers cannot read (even when provided with a lexicon) and also generated synthetic