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A Human Interactive Proof Algorithm Using Handwriting Recognition

A Human Interactive Proof Algorithm Using Handwriting Recognition,10.1109/ICDAR.2005.18,Amalia I. Rusu,Venu Govindaraju

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A Human Interactive Proof Algorithm Using Handwriting Recognition   (Citations: 5)
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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.
Conference: International Conference on Document Analysis and Recognition - ICDAR , pp. 967-971, 2005
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    • ...However, researchers in [4-6, 12] provide evidence of the cognitive ability of the human mind to correctly interpret highly distorted images or shapes, useful in applications such as human interactive proof systems and CAPTCHAs (Figure 2). Using the high success rate as evidence that the humans use Gestalt principles, such as closure, in order to interpret distorted images, we predict that our minimal effects will not hinder readability; ...

    Amalia Rusu, et al. Using the Gestalt Principle of Closure to Alleviate the Edge Crossing ...

    • ...We are motivated to use cognitive principles based on the success in using them in earlier work for handwritten CAPTCHAs [1], [2]...
    • ...These systems exploit human strengths over machines in interpreting imperfect visual objects and follow from earlier work on Gestalt and Geon transformed handwritten CAPTCHAs [1], [2]...
    • ...We have found that when applying transforms that take advantage of human cognition, machine recognition rates are very poor while human recognition does not suffer [1], [2]...
    • ...CAPTCHAs [1], [2], the Tree and Shape CAPTCHAs also utilize the Gestalt and Geon principles to transform image objects...
    • ...These same transformations presented the least difficulty for human subjects, based on the Gestalt laws of closure and continuity [1], [2]...
    • ...We are motivated by previous results showing very poor performance of machines on handwritten images using similar image transformations [1], [2]...

    Amalia I. Rusu, et al. Securing the Web Using Human Perception and Visual Object Interpretati...

    • ...Researchers at the University of Bualo, CEDAR have contributed a great deal of research building o of Naor’s 8th recommendation (\Handwriting recognition") [72] [73] [74] [75] [76]...

    Kurt Alfred Kluever. Securely Extending Tag Sets to Improve Usability in a Video-Based Huma...

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