Adaptive Classifier Construction: An Approach to Handwritten Digit Recognition

  • Tuan Trung Nguyen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2475)

Abstract

Optical Character Recognition (OCR) is a classic example of decision making problem where class identities of image objects are to be determined. This concerns essentially of finding a decision function that returns the correct classification of input objects. This paper proposes a method of constructing such functions using an adaptive learning framework, which comprises of a multilevel classifier synthesis schema. The schema’s structure and the way classifiers on a higher level are synthesized from those on lower levels are subject to an adaptive iterative process that allows to learn from the input training data. Detailed algorithms and classifiers based on similarity and dissimilarity measures are presented. Also, results of computer experiments using described techniques on a large handwritten digit database are included as an illustration of the application of proposed methods.

Keywords

Pattern recognition handwritten digit recognition clustering decision support systems machine learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Michael R. Anderberg. Cluster Analysis for Applications. Academic Press, Inc., 1973.Google Scholar
  2. 2.
    Jan Bazan, Hung Son Nguyen, Tuan Trung Nguyen, Jaroslaw Stepaniuk, and Andrzej Skowron. Application of modal logics and rough sets for classifying objects. In Michel De Glas and Zdzislaw Pawlak, editors, Proceedings of the Second World Conference on the Fundamentals of Artificial Intelligence, pages 15–26, Paris, France, 1995. Ankor.Google Scholar
  3. 3.
    J. Geist, R. A. Wilkinson, S. Janet, P. J. Grother, B. Hammond, N. W. Larsen, R. M. Klear, C. J. C. Burges, R. Creecy, J. J. Hull, T. P. Vogl, and C. L. Wilson. The second census optical character recognition systems conference. NIST Technical Report NISTIR 5452, pages 1–261, 1994.Google Scholar
  4. 4.
    K. Komori, T. Kawatani, K. Ishii, and Y. Iida. A feature concentrated method for character recognition. IFIP Proceedings, pages 29–34, 1977.Google Scholar
  5. 5.
    Z.C. Li, C.Y. Suen, and J. Guo. Hierarchical models for analysis and recognition of handwritten characters. Annals of Mathematics and Artificial Intelligence, pages 149–174, 1994.Google Scholar
  6. 6.
    L. Polkowski and A. Skowron. Towards adaptive calculus of granules. In L.A. Zadeh and J. Kacprzyk, editors, Computing with Words in Information/Intelligent Systems, pages 201–227, Heidelberg, 1999. Physica-Verlag.Google Scholar
  7. 7.
    Robert J. Schalkoff. Pattern Recognition: Statistical, Structural and Neural Approaches. John Wiley & Sons, Inc., 1992.Google Scholar
  8. 8.
    Kodratoff Y. and Michalski R. Machine Learning: An Artificial Intelligence Approach, volume 3. Morgan Kaufmann, 1990.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Tuan Trung Nguyen
    • 1
  1. 1.Polish-Japanese Institute of Information TechnologyWarsawPoland

Personalised recommendations