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A Symbol Classifier Able to Reject Wrong Shapes for Document Recognition Systems

  • Éric Anquetil
  • Bertrand Coüasnon
  • Frédéric Dambreville
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1941)

Abstract

We propose in this paper a new framework to develop a transparent classifier able to deal with reject notions. The generated classifier can be characterized by a strong reliability without loosing good properties in generalization. We show on a musical scores recognition system that this classifier is very well suited to develop a complete document recognition system. Indeed this classifier allows them firstly to extract known symbols in a document (text for example) and secondly to validate segmentation hypotheses. Tests had been successfully performed on musical and digit symbols databases.

Keywords

Document Recognition Symbol Recognition Musical Score Classification Genetic Algorithm Radial Basis Function Neural Networks Reliability 

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References

  1. 1.
    E. Anquetil and G. Lorette. Automatic generation of hierarchical fuzzy classification systems based on explicit fuzzy rules deduced from possibilistic clustering: Application to on-line handwritten character recognition. In Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU96), pages 259–264, 1996. 213Google Scholar
  2. 2.
    D. Bainbridge and N. P. Carter. Automatic reading of music notation. In P. S. P. Wang H. Bunke, editor, Handbook of Character Recognition and Document Image Analysis, pages 583–603. World Scientific, 1997. 212Google Scholar
  3. 3.
    J. C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, 1981. 213zbMATHGoogle Scholar
  4. 4.
    C. M. Bishop. Neural networks for pattern recognition. Oxford University Press Inc., 1995. 212Google Scholar
  5. 5.
    A. K. Chhabra. Graphic symbol recognition: An overview. In K. Tombre and A. K. Chhabra, editors, Graphics Recognition, Algorithms and Systems, number 1389 in LNCS. Springer, 1998. 209Google Scholar
  6. 6.
    B. Coüasnon and J. Camillerapp. Using grammars to segment and recognize music scores. In L. Spitz and A. Dengel, editors, Document Analysis Systems. World Scientific, 1995. 210Google Scholar
  7. 7.
    B. Coüasnon and J. Camillerapp. A way to separate knowledge from program in structured document analysis: application to optical music recognition. In ICDAR, International Conference on Document Analysis and Recognition, volume 2, pages 1092–1097, Montréal, Canada, August 1995. 211Google Scholar
  8. 8.
    David E. Goldberg. Genetic algorithms in search, optimization and machine learning. Addison-Wesley, 1989. 214Google Scholar
  9. 9.
    Simon Haykin. Neural Networks, a comprehensive foundation. Prentice Hall, 1997. 212Google Scholar
  10. 10.
    R. Krishnapuram. Generation of membership functions via possibilistic clustering. In IEEE World congress on computational intelligence, pages 902–908, 1994. 213Google Scholar
  11. 11.
    R. P. Lippmann. Pattern classification using neural networks. IEEE Communications Magazine, 27:47–64, 1989. 213CrossRefGoogle Scholar
  12. 12.
    V. Poulain d’Andecy, J. Camillerapp, and I. Leplumey. Kalman filtering for segment detection: application to music scores analysis. In ICPR, 12th International Conference on Pattern Recognition (IAPR), volume 1, pages 301–305, Jrusalem, Israel, October 1994. 212Google Scholar
  13. 13.
    Ching Y. Suen Shunji Mori and Kazuhiko Yamamoto. Historical review of ocr research and development. Proceedings of the IEEE, 80(7), July 1992. 212Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Éric Anquetil
    • 1
  • Bertrand Coüasnon
    • 1
  • Frédéric Dambreville
    • 1
  1. 1.Insa-Département Informatique 20IrisaRennes CedexFrance

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