Devnagari Handwritten Numeral Recognition using Geometric Features and Statistical combination Classifier
Journal Title: International Journal on Computer Science and Engineering - Year 2013, Vol 5, Issue 10
Abstract
This paper presents a Devnagari Numerical recognition method based on statistical discriminant functions. 17 geometric features based on pixel connectivity, lines, line directions, holes, image area, perimeter, eccentricity, solidity, orientation etc. are used for representing the numerals. Five discriminant functions viz. Linear, Quadratic, Diaglinear, Diagquadratic and Mahalanobis distance are used for classification. 1500 handwritten numerals are used for training. Another 1500 handwritten numerals are used for testing. Experimental results show that Linear, Quadratic and Mahalanobis discriminant functions provide better results. Results of these three Discriminants are fed to a majority voting type Combination classifier. It is found that Combination classifier offers better results over individual classifiers.
Authors and Affiliations
Vikas J. Dongre , Vijay H. Mankar
Keywords
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- EP ID EP120619
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