Summary
Optical character recognition (OCR) is a classic example of a decision making problem where class identities of image objects are to be determined. This concerns essentially finding a decision function that returns the correct classification of input objects. This chapter proposes a method of constructing such functions by using an adaptive learning framework based on 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 learning from input training data. Detailed algorithms and classifiers based on similarity and dissimilarity measures are presented. Also, results of computer experiments using the techniques described on a large handwritten digit database are included as an illustration of the application of the proposed methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Reference
M. R. Anderberg.Cluster Analysis for Applications.Academic Press, New York, 1973.
J. Bazan, H. S. Nguyen, T. T. Nguyen, J. Stepaniuk, A. Skowron. Application of modal logics and rough sets for classifying objects. In M. De Glas, Z. Pawlak, editorsProceedings of the Second World Conference on the Fundamentals of Artificial Intelligence (WOCFAI’95)15–26, Ankor, Paris, 1995.
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, C. L. Wilson. The second census optical character recognition systems conference. NIST Technical Report NISTIR 5452, 1–261, 1994.
K. Komori, T. Kawatani, K. Ishii, Y. Iida. A feature concentrated method for character recognition. In B. Gilchrist, editorIFIP ProceedingsNorth Holland, Amsterdam, 2934, 1977.
H. S. Nguyen, T. T. Nguyen. An approach to the handwriting digit recognition problem based on modal logic. Master’s Thesis, Institute of Mathematics, Warsaw University, 1993.
L. Polkowski, A. Skowron. Towards adaptive calculus of granules. In L.A. Zadeh, J. Kacprzyk, editorsComputing with Words in Information/Intelligent Systems201227, Physica, Heidelberg, 1999.
R. J. Schalkoff.Pattern Recognition: Statistical Structural and Neural Approaches. Wiley, New York, 1992.
Y. Kodratoff, R. Michalski.Machine Learning: An Artificial Intelligence ApproachVol. 3. Morgan Kaufmann, San Francisco, 1990.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Nguyen, T.T. (2004). Handwritten Digit Recognition Using Adaptive Classifier Construction Techniques. In: Pal, S.K., Polkowski, L., Skowron, A. (eds) Rough-Neural Computing. Cognitive Technologies. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18859-6_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-18859-6_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-62328-8
Online ISBN: 978-3-642-18859-6
eBook Packages: Springer Book Archive