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Biometric and Forensic Aspects of Digital Document Processing

  • Sargur N. Srihari
  • Chen Huang
  • Harish Srinivasan
  • Vivek Shah
Chapter
Part of the Advances in Pattern Recognition book series (ACVPR)

Keywords

Document Image Kullback Leibler False Reject Rate Stroke Width Handwritten Document 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Srihari, S.N., Cha, S., Arora, H., and Lee, S. (2002). Individuality of hand-writing. Journal of Forensic Sciences, 47(4), pp. 856-872.Google Scholar
  2. 2.
    Franke, K., Schomaker, L., Vuurpijl, L., and Giesler, S. (2003). Fish-new: a common ground for computer-based forensic writer identification. Proceedings of the Third European Academy of Forensic Science Triennial Meeting, Istanbul, Turkey, p. 84.Google Scholar
  3. 3.
    Srihari, S.N., Zhang, B., Tomai, C., Lee, S., Shi, Z., and Shin, Y.C. (2003). A system for hand-writing matching and recognition. Proceedings of the Symposium on Document Image Understanding Technology (SDIUT), Greenbelt, MD.Google Scholar
  4. 4.
    Huber, R. and Headrick, A. (1999). Handwriting Identification: Facts and Fundamentals. Boca Raton, FL: CRC Press.Google Scholar
  5. 5.
    Srikantan, G., Lam, S., and Srihari, S.N. (1996). Gradient-based contour encoding for character recognition. Pattern Recognition, 7, pp. 1147-1160.CrossRefGoogle Scholar
  6. 6.
    Zhang, B. and Srihari, S.N. (2003). Analysis of handwriting individuality using handwritten words. Proceedings of the Seventh International Conference on Document Analysis and Recognition, Edinburgh, Scotland.Google Scholar
  7. 7.
    Zhang, B., Srihari, S.N., and Lee, S.-J. (2003). Individuality of handwritten characters. Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR), pp. 1086-1090.Google Scholar
  8. 8.
    Osborn, A.S. (1029). Questioned Documents. London: Nelson Hall Pub.Google Scholar
  9. 9.
    Champod, C. (1999). The inference of identity of source: theory and practice. The First International Conference on Forensic Human Identification in the Millennium, London, UK, October 1999, pp. 24-26.Google Scholar
  10. 10.
    Tippett, C.F., Emerson, V.J., Fereday, M.J., Lawton, F., and Lampert, S.M. (1968). The evidential value of the comparison of paint flakes from sources other than vehicules. Journal of Forensic Sciences Society, 8, pp. 61-65.CrossRefGoogle Scholar
  11. 11.
    Plamondon, R. and Srihari, S.N. (2000). On-line and off-line handwriting recognition: a comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), pp. 63-84.CrossRefGoogle Scholar
  12. 12.
    Plamondon, R. and Lorette, G. (2000). On-line and off-line handwriting recognition: a comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), pp. 63-84.CrossRefGoogle Scholar
  13. 13.
    Kalera, M.K., Zhang, B., and Srihari, S.N. (2003). Off-line signature verification and identification using distance statistics. Proceedings of the International Graphonomics Society Conference, Scottsdale, AZ, pp. 228-232.Google Scholar
  14. 14.
    Horn, B. (1986). Robot Vision. Cambridge, MA: MIT Press.Google Scholar
  15. 15.
    Munich, M.E. and Perona, P. (2003). Visual identification by signature tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(2), pp. 200-217.CrossRefGoogle Scholar
  16. 16.
    Fang, B., Leung, C.H., Tang, Y.Y., Tse, K.W., Kwok, P.C.K., and Wong, Y.K. (2003). Off-line signature verification by the tracking of feature and stroke positions. Pattern Recognition, 36, pp. 91-101.zbMATHCrossRefGoogle Scholar
  17. 17.
    Lee, S. and Pan, J.C. (1992). Off-line tracing and representation of signatures. IEEE Transactions on Systems, Man and Cybernetics, 22, pp. 755-771.zbMATHCrossRefGoogle Scholar
  18. 18.
    Sabourin, R. and Plamondon, R. (1986). Preprocessing of handwritten signatures from image gradient analysis. Proceedings of the Eighth International Conference on Pattern Recognition, pp. 576-579.Google Scholar
  19. 19.
    Lin, C.C. and Chellappa, R. (1997). Classification of partial 2-d shapes using fourier descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9, pp. 696-690.Google Scholar
  20. 20.
    Ammar, M., Yoshido, Y., and Fukumura, T. (1986). A new effective approach for off-line verification of signatures by using pressure features. Proceedings of the Eighth International Conference on Pattern Recognition, pp. 566-569.Google Scholar
  21. 21.
    Guo, J.K., Doermann, D., and Rosenfeld, A. (1997). Local correspondence for detecting random forgeries. Proceedings of the International Conference on Document Analysis and Recognition, pp. 319-323.Google Scholar
  22. 22.
    Xu, A., Kalera, M.K., and Srihari, S.N. (2004). Learning strategies and classification methods for off-line signature verification. Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition, pp. 161-166.Google Scholar
  23. 23.
    Zhang, B. and Srihari, S. (2003). Binary vector dissimilarity measures for handwriting identification. Proceedings of SPIE, Document Recognition and Retrieval X, pp. 155-166.Google Scholar
  24. 24.
    Joachims, T. (1998). Text categorization with support vector machines: learning with many relevant features. Proceedings of the European Conference on Machine Learning, pp. 137-142.Google Scholar
  25. 25.
    Osuna, E., Freund, R., and Girosi, F. (1997). Support vector machines: training and applications. Technical Report AIM-1602. MIT.Google Scholar
  26. 26.
    Boser, B., Guyon, I., and Vapnik, V. (1992). A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory.Google Scholar

Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  • Sargur N. Srihari
    • 1
  • Chen Huang
    • 2
  • Harish Srinivasan
    • 3
  • Vivek Shah
    • 4
  1. 1.Center of Excellence for Document Analysis and Recognition (CEDAR)University at Buffalo, State University of New YorkAmherstUSA
  2. 2.Center of Excellence for Document Analysis and Recognition (CEDAR)University at Buffalo, State University of New YorkAmherstUSA
  3. 3.Center of Excellence for Document Analysis and Recognition (CEDAR)University at Buffalo, State University of New YorkAmherstUSA
  4. 4.Center of Excellence for Document Analysis and Recognition (CEDAR)University at Buffalo, State University of New YorkAmherstUSA

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