Zoning design for handwritten numeral recognition

  • G. Dimauro
  • S. Impedovo
  • G. Pirlo
  • A. Salzo
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)


This paper presents a new approach for zoning design. The approach is based on a techinque which detects the most discriminant image regions by the analysis of feature distributions, and obtains the zoning by an iterative zone-growing process. An application to handwritten numeral recognition is also reported showing the effectiveness of the proposed approach.


Handwriting Recognition Discrimination Capability Handwritten Digit Recognition Handwritten Numeral Handwritten Word Recognition 
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.


  1. 1.
    C.Y.Suen, M. Berthod, S. Mori, “Computer Recognition of Handprinted Characters: The State of the Art”, Proc. of IEEE, 68 (4), pp. 469–483, 1980.Google Scholar
  2. 2.
    G.Baptista, K.M.Kulkarni, “A high accuracy algorithm for recognition of handwritten numerals”, Pattern Recognition 4, pp.287–291, 1988.CrossRefGoogle Scholar
  3. 3.
    B. Hussain and M. R. Kabuka, “A Novel Feature Recognition Neural Network and its Application to Character Recognition”, IEEE Transactions on Pattern Analysis Machine Intelligence, vol. 16, no. 1, pp. 98–106, Jan 1994.CrossRefGoogle Scholar
  4. 4.
    P. Ahmed and C. Y. Suen, “Computer Recognition of Totally Uncostrained Handwritten Zip Codes”, International Journal of Pattern Recognition and Artificial intelligence, vol. 1, no. 1, pp. 1–15, 1987.CrossRefGoogle Scholar
  5. 5.
    M. Yen Cen, A. Kundu and J. Zhou, “Off-Line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network”, IEEE Transactions on Pattern Analysis Machine Intelligence, vol. 16, no. 5, pp. 481–496, May 1994.CrossRefGoogle Scholar
  6. 6.
    Ley Xu, Adam Krzyzak, Ching Y-Suen, “Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition“, IEEE Transaction on Systems, Man and Cybernetics, Vol. 22, N. 3, 1992, pp. 418–435.Google Scholar
  7. 7.
    Hull, T.K.Ho, J. Favata, V. Govindaraju, S. Srihari, “Combination of Segmentation-based and Wholistic Handwritten Word Recognition Algorithms”, in From Pixels to Feature III-Frontiers in Handwriting Recognition, S. Impedovo and J.C. Simon eds., Elsevier Publ., pp. 261–272, 1992.Google Scholar
  8. 8.
    Huang, C.Y. Suen, “An Optimal Method of Combining Multiple Classifiers for Unconstrained Handwritten Numeral Recognition”, Proc. of IWFHR-3, Buffalo, NY, 1993, pp. 11–20.Google Scholar
  9. 9.
    Y. Lu, F. Yamaoka, “Integration of Handwritten Digit Recognition results using Evidential Reasoning”, Proc. of IWFHR-4, 1994, pp. 456–463.Google Scholar
  10. 10.
    G. Dimauro, S. Impedovo, G. Pirlo, A. Salzo, “A multi-expert system to handwritten digit recognition”, in Progress in Handwriting Recognition, S. Impedovo Ed., World Scientific, 1997, pp. 363–367.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • G. Dimauro
    • 1
  • S. Impedovo
    • 1
  • G. Pirlo
    • 1
  • A. Salzo
    • 1
  1. 1.Dipartimento di InformaticaUniversità di BariBariItaly

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