A hierarchical classifier for multifont digits

  • C. Rodriguez
  • J. Muguerza
  • M. Navarro
  • A. Zárate
  • J. I. Mar'in
  • J. M. Pérez
Poster Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)

Abstract

In this paper, the automatic recognition of broken and blurred, multifont typewritten digits in forms will be addressed. The classification, which is based on the utilization of a global feature, is divided in two phases: first, a minimum distance method (1-NN) is applied to provide a global classification of the patterns in a form; second, the patterns in the form previously classified are used to validate, or reject and reclassify them, on the basis of the mean distance to the predefined classes. In this way, a classification accuracy rate of 99.42% has been achieved.

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References

  1. [1]
    M.A. Abou-Nasr, M.A. Sid-Ahmed: Fast Learning and Efficient Memory Utilization with a Prototype Based Neural Classifier. Pattern Recognition, Vol. 28, No. 4, pp. 581–593, 1995.CrossRefGoogle Scholar
  2. [2]
    F.H. Cheng, W.H. Hsu, M.C. Kuo: Recognition of Handprinted Chinese Characters Via Stroke Relaxation. Pattern Recognition, Vol. 26, No. 4, pp. 579–593, 1993.CrossRefGoogle Scholar
  3. [3]
    Z. Chi, J. Wu, H. Yan: Handwritten Numeral Recognition Using Self-Organizing Maps and Fuzzy Rules. Pattern Recognition, Vol. 28, No. 1, pp. 59–66, 1995.CrossRefGoogle Scholar
  4. [4]
    B.V. Dasarathy: Nearest Neighbor(NN) Norms: NN Pattern Classification Techniques. Ed: IEEE Computer Society Press, 1991.Google Scholar
  5. [5]
    S.A. Dudani, K.J. Breeding, R.B. McGhee: Aircraft Identification by Moment Invariants. IEEE Transactions on Computers, Vol. C-26, No. 1, pp. 39–45, January 1977.Google Scholar
  6. [6]
    M.D. Garris, C.L. Wilson, J.L. Blue, G.T. Candela, P. Grother, S. Janet, R.A. Wilkinson: Massively Parallel Implementation of Character Recognition Systems. NIST: SPIE's Conference on Character Recognition and Digitizer Technologies, Vol. 1661, pp. 269–280, February 1992.Google Scholar
  7. [7]
    J. Geist, R.A. Wilkinson, S. Janet, P. Grother, B. Hammond, N.W. Larsen, R.M. Klear, M.J. Matsko, C.J.C. Burges, R Creecy, J.J. Hull, T.P. Vogl, C.L. Wilson: The Second Census Optical Character Recognition Systems Conference. NIST: Technical Report NISTIR 5452, National Institute of Standards Technology, May 1994.Google Scholar
  8. [8]
    P. Grother, G.T. Candela: Comparison of Handprinted Digit Classifiers. NIST Technical Report NISTIR 5209, National Institute of Standards Technology, June 1993.Google Scholar
  9. [9]
    S. Impedovo, L. Ottaviano, S. Occhinegro: Optical Character Recognition — A Survey. Character & Handwriting Recognition, Ed: P.S.P. Wang, pp. 1–24, World Scientific series in Computer Science, Vol. 30, 1991.Google Scholar
  10. [10]
    S. Knerr, L. Personnaz, G. Dreyfus: Handwritten Digit Recognition by Neural Networks with Single-Layer Training. IEEE Transactions on Neural Networks, Vol. 3, No. 6, pp. 962–968, November 1992.CrossRefGoogle Scholar
  11. [11]
    R.P. Lippmann: An Introduction to Computing with Neural Nets. IEEE ASSP, pp. 4–22, April 1987.Google Scholar
  12. [12]
    O. Matan, H.S. Baird, J. Bromley, C.J.C. Burges, J.S. Denker, L.D. Jackel, Y. Le Cun, E.D.P. Pednault, W.D. Satterfield, C.E. Stenard, T.J. Thompson: Reading Handwritten Digits: A ZIP Code Recognition System. IEEE Computer, pp. 59–63, July 1992.Google Scholar
  13. [13]
    D. Michie, D.J. Spiegelhalter, C.C. Taylor: Machine Learning, Neural and Statistical Classification. Ed: Ellis Horwood Series in Artificial Intelligence, 1994.Google Scholar
  14. [14]
    S. Mori, C.Y. Suen, K. Yamamoto: Historical Review of OCR Research and Development. Proceedings of the IEEE, Vol. 80, No. 7, pp. 1029–1058, July 1992 (Special Issue on Optical Character Recognition).CrossRefGoogle Scholar
  15. [15]
    J. Muguerza: Una Solución al Reconocimiento Automático de Digitos Imprecisos en Formularios. Tesis Doctoral, Departamento de Arquitectura y Tecnología de Computadores, Universidad del Pais Vasco, Enero 1996.Google Scholar
  16. [16]
    L. O'Gorman, R Kasturi: Document Image Analysis Systems. Introduction. IEEE Computer, pp. 5–8, July 1992.Google Scholar
  17. [17]
    D.E. Rumelhart, G.E. Hinton, R.J. Williams: Learning Internal Representations by Error Propagation. D.E. Rumelhart, J. MacClelland (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1: Foundations. The Massachusetts Institute of Technology, Cambridge, hMA, USA, 1986.Google Scholar
  18. [18]
    P.K. Simpson: Artificial Neural Systems: Foundations, Paradigms, Applications and Implementations. Ed: Pergamon Press, 1990.Google Scholar
  19. [19]
    S.N. Srihari: High-Performance Reading Machines. Proceedings of the IEEE, Vol. 80, No. 7, pp. 1120–1132, July 1992 (Special Issue on Optical Character Recognition).CrossRefGoogle Scholar
  20. [20]
    C.Y. Suen, M. Berthod, S. Mori: Automatic Recognition of Handprinted CharactersThe State of Art. Proceedings of the IEEE, Vol. 68, No. 4, pp. 469–487, April 1980.Google Scholar

Copyright information

© Springer-Verlag 1998

Authors and Affiliations

  • C. Rodriguez
    • 1
  • J. Muguerza
    • 1
  • M. Navarro
    • 1
  • A. Zárate
    • 1
  • J. I. Mar'in
    • 1
  • J. M. Pérez
    • 1
  1. 1.Computer Architecture and Technology DepartmentThe Basque Country University (UPV/EHU)DonostiaSpain

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