A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical Character Recognition: An Illustrated Guide to the Frontier
Optical Character Recognition: An Illustrated Guide to the Frontier
Use of Lexicon Density in Evaluating Word Recognizers
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Dependence of Handwritten Word Recognizers on Lexicons
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Human Interactive Proofs and Document Image Analysis
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Reading handwritten US census forms
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Pessimal Print: A Reverse Turing Test
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
ARTiFACIAL: automated reverse turing test using FACIAL features
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Embedded noninteractive continuous bot detection
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
Synthetic handwritten CAPTCHAs
Pattern Recognition
Leveraging cognitive factors in securing WWW with CAPTCHA
WebApps'10 Proceedings of the 2010 USENIX conference on Web application development
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The recognition of unconstrained handwriting continues to be a difficult task for computers despite active research for several decades. This is because handwritten text offers great challenges such as: character and word segmentation, character recognition, variation between handwriting styles, different character size and orientation, no font constraints, the type of printing surface, as well as the background clarity. In this paper we explore the gap in the ability in reading handwritten text between humans and computers to propose solutions for security problems in Web Services. We present a new HIP algorithm that uses handwriting recognition task to distinguish between humans and computers. We propose methods to deform handwritten text images to make them indecipherable by computers and explore the cognitive factors that assist humans in reading and understanding. Experimental results on both humans and computers are presented and compared.