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An Improved Learning Scheme for the Moving Window Classifier for Handwritten Character Recognition

Hoque, M.S. and Fairhurst, M.C. (2001) An Improved Learning Scheme for the Moving Window Classifier for Handwritten Character Recognition. In: Proceedings of Sixth International Conference on Document Analysis and Recognition. IEEE, pp. 607-611. ISBN 0-7695-1263-1. (doi:10.1109/ICDAR.2001.953861) (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)

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. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/ICDAR.2001.953861

Abstract

The moving window classifier (MWC) is a simple and efficient classifier structure which, although shown to be capable of promising performance in a variety of tasks such as face recognition, its common application is a tool in text recognition. Various measures have been proposed to improve the MWC classification speed and to reduce memory space requirement. This paper introduces techniques for improving the MWC classification accuracy without losing any of gains previously achieved. These performance enhancement schemes are readily applicable to a range of related classifiers and hence provide a generalized method for enhancement in a variety of tasks.

Item Type: Book section
DOI/Identification number: 10.1109/ICDAR.2001.953861
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image Analysis, Image Processing
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Yiqing Liang
Date Deposited: 11 Aug 2009 09:26 UTC
Last Modified: 01 Aug 2019 10:30 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/6472 (The current URI for this page, for reference purposes)
Hoque, M.S.: https://orcid.org/0000-0001-8627-3429
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