Generation and use of handwritten CAPTCHAs

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 and bank check reading, in this paper, we explore the use of handwriting recognition in designing CAPTCHAs for cyber security. CAPTCHAs (Completely Automatic Public Turing tests to tell Computers and Humans Apart) are automatic reverse Turing tests designed so that virtually all humans can pass the test, but state-of-the-art computer programs will fail. Machine-printed, text-based CAPTCHAs are now commonly used to defend against bot attacks. Our focus is on exploring the generation and use of handwritten CAPTCHAs. We have used a large repository of handwritten word images that current handwriting recognizers cannot read (even when provided with a lexicon) for this purpose and also used synthetic handwritten samples. We take advantage of both our knowledge of the common source of errors in automated handwriting recognition systems as well as the salient aspects of human reading. The simultaneous interplay of several Gestalt laws of perception and the geon theory of pattern recognition (that implies object recognition occurs by components) allows us to explore the parameters that truly separate human and machine abilities.

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