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

Rahman, A.F.R. and Fairhurst, M.C. (1997) Building multiple prototype classifiers for handwritten character recognition using automatic statistical clustering techniques. In: Sixth International Conference on Image Processing and its Applications, Dublin, Ireland. (The full text of this publication is not available from this repository)

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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: Conference or workshop item (Other)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics (see also: telecommunications)
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts
Depositing User: T.J. Sango
Date Deposited: 21 May 2009 07:19
Last Modified: 08 May 2012 10:55
Resource URI: http://kar.kent.ac.uk/id/eprint/17897 (The current URI for this page, for reference purposes)
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