Performance Evaluation of GMM and SVM for Recognition of Hierarchical Clustering Character

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)

Abstract

This paper presents an approach for performance evaluation of hierarchical clustering character and recognition of handwritten characters. The approach uses as an efficient feature called Character Intensity Vector. A hierarchical recognition methodology based on the structural details of the character is adopted. At the first level similar structured characters are grouped together and the second level is used for individual character recognition. Gaussian Mixture Model and Support Vector Machine are used in first level and second level classifiers and evaluate the accuracy performance of the handwritten characters. Gaussian Mixture Model is used for classification which achieves an overall accuracy of character level 94.39% and Support Vector Machine which achieves an overall accuracy of character level 93.61% is achieved.

Keywords

Handwritten Character Recognition Character Intensity Vector(CIV) Hierarchical Character Clustering Support Vector Machine(SVM) Gaussian Mixture Model(GMM) 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAnnamalai UniversityChidambaramIndia

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