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Combining high-level features with sequential local features for on-line handwriting recognition

  • Jianying Hu
  • Amy S. Rosenthal
  • Michael K. Brown
Poster Session D: Biomedical Applications, Detection, Control & Surveillance, Inspection, Optical Character Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)

Abstract

The trade-off between high-level, long-range features and low level, local features is common among many pattern recognition problems: the former are usually more powerful but less robust, while the latter is less informative but more reliable. In this paper we describe a new method for combining high-level long-range features and local features for on-line handwriting recognition. First, high-level features such as crossings, loops and cusps are extracted. A localization procedure is then applied to spread these high-level features over the neighboring sample points, resulting in local representations of nearby high-level features. These features are then combined with the usual local features at each sample point and used in an integrated segmentation and recognition process. This method allows incorporation of information carried by high-level long-range features while at the same time maintains the high reliability of the recognition system. We report experimental results on an HMM based recognizes for writer independent recognition of unconstrained handwritten words.

Keywords

Local Feature Handwriting Recognition Handwritten Word Handwriting Recognition System Cusp Distance 
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.

References

  1. 1.
    S. A. Guberman and V. V. Rozentsveig. Algorithm for the recognition of handwritten text. Automation and Remote Control, 37 37(5):751–757, May 1976.Google Scholar
  2. 2.
    S. Bercu and G. Lorette. On-line handwritten word recognition: An approach based on hidden markov models. In Proceeding Third Int. Workshop on Frontiers in Handwriting Recognition, pages 385–390, Buffalo, USA, May 1993.Google Scholar
  3. 3.
    J. Hu, M. K. Brown, and W. Turin. Handwriting recognition with hidden Markov models and grammatical constraints. In Proc. 4th IWFHR, pages 195–205, Taipei, Taiwan, December 1994.Google Scholar
  4. 4.
    J. Makhoul, T. Starner, R. Schartz, and G. Chou.On-line cursive handwriting recognition using speech recognition methods. In Proc. IEEE ICASSP'94, pages v125–v128, Adelaide, Australia, April 1994.Google Scholar
  5. 5.
    K. S. Nathan, H. S. M. Beigi, J. Subrahmonia, G. J. Clary, and H. Maruyama. Real-time on-line unconstrained handwriting recognition using statistical methods. In Proc. IEEE ICASSP'95, pages 2619–2622, Detroit, USA, June 1995.Google Scholar
  6. 6.
    S. Manke and U. Bodenhausen. Npen++: A writer independent, large vocabulary on-line cursive handwriting recognition system. In Prod. 3rd ICDAR, pages 403-408, Montreal, Canada, August 1995.Google Scholar
  7. 7.
    M. Schenkel, I. Guyon, and D. Henderson. On-line cursive script recognition using time delay neural networks and hidden Markov models. Machine Vision and Applications, Special Issue on Cursive Script Recognition, 8, 1995.Google Scholar
  8. 8.
    S. Manke, M. Finke, and A. Waibel. Combining bitmaps with dynamic writing information for on-line handwriting recognition. In Prod. 12th ICPR, pages 596-598, Jerusalem, October 1994.Google Scholar
  9. 9.
    J. Hu, M. K. Brown, and W. Turin. Use of segmental features in HMM based handwriting recognition. In Proc. IEEE SMC'95, pages 2778–2782, Vancouver, Canada, October 1995.Google Scholar
  10. 10.
    J. Hu, M.K. Brown, and W. Turin. HMM based on-line handwriting recognition. IEEE PAMI, 18(10):1039–1045, October 1996.Google Scholar
  11. 11.
    Amy S. Rosenthal, J. Hu, and M. K. Brown. Size and orientation normalization of on-line handwriting using Hough transform. In Proc. ICASSP'97, to appear, Munich, Germany, April 1997.Google Scholar
  12. 12.
    J. Hu, M. K. Brown, and W. Turin. Invariant features for HMM based handwriting recognition. In Proc. ICIAP'95, pages 588–593, Sanremo, Italy, September 1995.Google Scholar
  13. 13.
    L. R. Rabiner and B. H. Juang. Fundamentals of Speech Recognition. Prentice Hall, Englewood Cliffs, NJ, 1993.Google Scholar
  14. 14.
    J. Hu and M. K. Brown. On-line handwriting recognition with constrained n-best decoding. In Proc. 13th ICPR, volume C, pages 23-27, Vienna, Austria, August 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Jianying Hu
    • 1
  • Amy S. Rosenthal
    • 2
  • Michael K. Brown
    • 1
  1. 1.Bell LaboratoriesLucent TechnologiesMurray HillUSA
  2. 2.Alpha Technologies Inc.PiscatawayUSA

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