Combining neural gas and learning vector quantization for cursive character recognition

Camastra, F. and Vinciarelli, A. (2003) Combining neural gas and learning vector quantization for cursive character recognition. Neurocomputing, 51(4), pp. 147-159. (doi:10.1016/S0925-2312(02)00613-6)

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Publisher's URL: http://dx.doi.org/10.1016/S0925-2312(02)00613-6

Abstract

This paper presents a cursive character recognizer, a crucial module in any Cursive Script Recognition system based on a segmentation and recognition approach. The character classification is achieved by combining the use of neural gas (NG) and learning vector quantization (LVQ). NG is used to verify whether lower and upper case version of a certain letter can be joined in a single class or not. Once this is done for every letter, it is possible to find an optimal number of classes maximizing the accuracy of the LVQ classifier. A database of 58000 characters was used to train and test the models. The performance obtained is among the highest presented in the literature for the recognition of cursive characters.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Vinciarelli, Professor Alessandro
Authors: Camastra, F., and Vinciarelli, A.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Neurocomputing
ISSN:0925-2312
Published Online:15 July 2002

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