The Efficient Feature Extraction of Handwritten Numerals in GLVQ Clustering Network


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 2, No. 6, pp. 995-1001, Nov. 1995
10.3745/KIPSTE.1995.2.6.995,   PDF Download:

Abstract

The structure of a typical pattern recognition consists a pre-processing, a feature extraction(algorithm) and classification or recognition. In classification, when widely varying patterns exist in same category, we need the clustering which organize the similar patterns. Clustering algorithm is two approaches. First, statistical approaches which are k-means, ISODATA algorithm. Second, neural network approach which is T.Kohonen''s LVQ(Learning Vector Quantization). Nikhil R. Palet al proposed the GLVQ(Generalized LVQ, 1993). This paper suggest the efficient feature extraction methods of handwritten numerals in GLVQ clustering network. We use the handwritten numeral data from 20''s authors(ie, 200 patterns) and compare the proportion of misclassified patterns for each feature extraction methods. As results, when we use the projection combination method, the classification ratio is 98.5%.


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Cite this article
[IEEE Style]
J. J. Won and M. J. Young, "The Efficient Feature Extraction of Handwritten Numerals in GLVQ Clustering Network," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 2, no. 6, pp. 995-1001, 1995. DOI: 10.3745/KIPSTE.1995.2.6.995.

[ACM Style]
Jeon Jong Won and Min Joon Young. 1995. The Efficient Feature Extraction of Handwritten Numerals in GLVQ Clustering Network. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 2, 6, (1995), 995-1001. DOI: 10.3745/KIPSTE.1995.2.6.995.