Using character recognition and segmentation to tell computer from humans

How do you tell a computer from a human? The situation arises often on the Internet, when online polls are conducted, accounts are requested, undesired email is received, and chat-rooms are spammed. The approach we use is to create a visual challenge that is easy for humans but difficult for a computer. More specifically, our challenge is to recognize a string of random distorted characters. To pass the challenge, the subject must type in the correct corresponding ASCII string. From an OCR point of view, this problem is interesting because our goal is to use the vast amount of accumulated knowledge to defeat the state of the art OCR algorithms. This is a role reversal from traditional OCR research. Unlike many other systems, our algorithm is based on the assumption that segmentation is much more difficult than recognition. Our image challenges present hard segmentation problems that humans are particularly apt at solving. The technology is currently being used in MSN's Hotmail registration system, where it has significantly reduced daily registration rate with minimal Consumer Support impact.

[1]  Manuel Blum,et al.  Telling Humans and Computers Apart Automatically or How Lazy Cryptographers do AI , 2002 .

[2]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[3]  Proceedings Seventh International Conference on Document Analysis and Recognition , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[4]  Yoshua Bengio,et al.  LeRec: A NN/HMM Hybrid for On-Line Handwriting Recognition , 1995, Neural Computation.

[5]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[6]  George Wolberg,et al.  Digital image warping , 1990 .

[7]  John Langford,et al.  Telling humans and computers apart automatically , 2004, CACM.

[8]  Roberto Pieraccini,et al.  Dynamic planar warping for optical character recognition , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[9]  Rachid Deriche,et al.  Fast algorithms for low-level vision , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[10]  Henry S. Baird,et al.  PessimalPrint: a reverse Turing test , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[11]  Kris Popat,et al.  Human Interactive Proofs and Document Image Analysis , 2002, Document Analysis Systems.

[12]  P. Danielsson Euclidean distance mapping , 1980 .

[13]  B. Barsky,et al.  An Introduction to Splines for Use in Computer Graphics and Geometric Modeling , 1987 .