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Proceedings Paper

Adaptive classifier based on K-means clustering and dynamic programing
Author(s): Antonio Navarro; Charles R. Allen
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Paper Abstract

Generally speaking, a recognition system should be insensitive to translation, rotation, scaling and distortion found in the data set. Non-linear distortion is difficult to eliminate. This paper discusses a method based on dynamic programming which copes with features normalization subjected to small non-linear distortions. Combining with k- means clustering results in a statistical classification algorithm suitable for pattern recognition problems. In order to assess the classifier, it has been integrated into a hand-written character recognition system. Dynamic features have been extracted from a database of 1248 isolated Roman character. The recognition rates are, on average, 91.67 percent and 94.55 percent. The classifier might also be tailored to any pattern recognition application.

Paper Details

Date Published: 3 April 1997
PDF: 8 pages
Proc. SPIE 3027, Document Recognition IV, (3 April 1997); doi: 10.1117/12.270077
Show Author Affiliations
Antonio Navarro, Univ. of Coimbra (Portugal)
Charles R. Allen, Univ. of Newcastle upon Tyne (United Kingdom)

Published in SPIE Proceedings Vol. 3027:
Document Recognition IV
Luc M. Vincent; Jonathan J. Hull, Editor(s)

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