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Building multiple prototype classifiers for handwritten character recognition using automatic statistical clustering techniques

Rahman, Ahmad Fuad Rezaur and Fairhurst, Michael (1997) Building multiple prototype classifiers for handwritten character recognition using automatic statistical clustering techniques. In: 1997 Sixth International Conference on Image Processing and Its Applications. IEEE, pp. 414-418. ISBN 978-0-85296-692-1. (doi:10.1049/cp:19970927) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:17897)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1049/cp:19970927

Abstract

Automatic statistical clustering techniques have been applied to implement different multiple prototype classifiers. Multiple prototyping offers an optimised solution to cases where there is significant variability in the training data. A typical application area is the recognition of handwritten characters. Once a set of features has been extracted, different statistical clustering techniques can be implemented to achieve multi-dimensional clustering in the feature space. Building of prototypes from these clusters is straight-forward, The success of the multi-prototyping depends on the efficiency of the statistical clustering techniques. In this paper, different clustering techniques have been used in conjunction with the use of different approaches to the formation of prototypes and the relative performance enhancements are reported.

Item Type: Book section
DOI/Identification number: 10.1049/cp:19970927
Uncontrolled keywords: handwriting recognition
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: T.J. Sango
Date Deposited: 21 May 2009 07:19 UTC
Last Modified: 16 Nov 2021 09:56 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/17897 (The current URI for this page, for reference purposes)

University of Kent Author Information

Fairhurst, Michael.

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