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HANDWRITTEN GURMUKHI CHARACTER RECOGNITION : M.Tech. Thesis
A Thesis
Submitted in Partial Fulfillment of the
Requirements for the Award of the Degree of
MASTER OF TECHNOLOGY
in
COMPUTER SCIENCE & ENGINEERING
By
Kartar Singh Siddharth
(Roll No. 09203008)
Under the Supervision of
| Dr Renu Dhir Associate Professor |
And | Mrs. Rajneesh Rani Assistant Professor |
DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING
DR B R AMBEDKAR NATIONAL INSTITUTE OF TECHNOLOGY
JALANDHAR
July 2011
Download the thesis report in pdf format:
Handwritten Gurmukhi Character Recognition: An M.Tech. Thesis Report
Performance Comparison of Devanagari Handwritten Numerals Recognition
Abstract:
In this paper an automatic recognition system for isolated Handwritten Devanagari Numerals is proposed and compared the recognition rate with different classifier. We presented a feature extraction technique based on recursive subdivision of the character image so that the resulting sub-images at each iteration have balanced numbers of foreground pixels as possible. Database, provided by Indian Statistical Institute, Kolkata, have 22547 grey scale images written by 1049 persons and obtained 98.98% highest accuracy with SVM classifier. Results are compared with KNN and Quadratic classifier.
Authors:
Mahesh Jangid, Kartar Singh, Renu Dhir, Rajneesh Rani
Keywords:
Devanagari Numeral, Indian Script, SVM (Support Vector Machine), KNN, Quadratic
Citation:
Mahesh Jangid, Kartar Singh, Renu Dhir, Rajneesh Rani, “Performance Comparison of Devanagari Handwritten Numerals Recognition”, International Journal of Computer Applications, Volume 22-No.1, pp. 1-6, May 2011.
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At IJCA Journal website
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Performance Comparison of Devanagari Handwritten Numerals Recognition
Kartar Singh Siddharth 