AN EFFICIENT FEATURE EXTRACTION AND CLASSIFICATION OF HANDWRITTEN DIGITS USING NEURAL NETWORKS

The wide range of shape variations for handwritten digits requires an adequate representation of thediscriminating features for classification. For the recognition of characters or numerals requires pixel valuesof a normalized raster image and proper features to reach very good classification rate....

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Bibliographic Details
Published in:International journal of computer science, engineering and applications engineering and applications, 2011-10, Vol.1 (5), p.47-47
Main Authors: VenkateswaraRao, N, Srikrishna, A, RaveendraBabu, B, Babu, Rama Mohan
Format: Article
Language:English
Online Access:Get full text
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Summary:The wide range of shape variations for handwritten digits requires an adequate representation of thediscriminating features for classification. For the recognition of characters or numerals requires pixel valuesof a normalized raster image and proper features to reach very good classification rate. This paper primarily concerns the problem of isolated handwritten numeral recognition of English scripts.Multilayer Perceptron(MLP) classifier is used for classification. The principalcontributions presented here are preprocessing, feature extraction and multilayer perceptron (MLP) classifiers.The strength of our approach is efficient feature extraction and the comprehensive classification scheme due to which, we have been able to achieve a recognition rate of 95.6, better than the previous approaches.
ISSN:2231-0088
2230-9616