BRIEF REVIEW OF RESEARCH ON DEVANAGARI SCRIPT

Holambe A.N.1*, Thool R.C.2*, Shinde U.B.3*, Holambe S.N.4*
1Department of Information Technology, College of Engineering, Osmanabad-413501, MS, India
2Department of Information Technology, Shri Guru Gobind Singhji Institute of Engineering & Technology, Vishnupuri, Nanded-431606, MS, India
3Savitribai Phule Women's Engineering College, Aurangabad, MS, India
4Department of Information Technology, College of Engineering, Osmanabad-413501, MS, India
* Corresponding Author : anholambe@yahoo.com

Received : -     Accepted : -     Published : 21-12-2010
Volume : 1     Issue : 2       Pages : 6 - 9
Int J Comput Intell Tech 1.2 (2010):6-9

Keywords : Devanagari script, KNN Classifier, accuracy, rejection, error percentage of classification
Conflict of Interest : None declared

Cite - MLA : Holambe A.N., et al "BRIEF REVIEW OF RESEARCH ON DEVANAGARI SCRIPT." International Journal of Computational Intelligence Techniques 1.2 (2010):6-9.

Cite - APA : Holambe A.N., Thool R.C., Shinde U.B., Holambe S.N. (2010). BRIEF REVIEW OF RESEARCH ON DEVANAGARI SCRIPT. International Journal of Computational Intelligence Techniques, 1 (2), 6-9.

Cite - Chicago : Holambe A.N., Thool R.C., Shinde U.B., and Holambe S.N. "BRIEF REVIEW OF RESEARCH ON DEVANAGARI SCRIPT." International Journal of Computational Intelligence Techniques 1, no. 2 (2010):6-9.

Copyright : © 2010, Holambe A.N., et al, Published by Bioinfo Publications. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

Abstract

In this paper we have done a brief survey of Devanagari script, research and different classifiers approach used for Character Recognition .We have collected and created database of handwritten characters. We have preprocessed it, extracted feature using local intensity distribution of gradient and used in our experiment. We have used KNN Classifier. Experiment result illustrates the accuracy, rejection, error percentage of classification.

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