CFP last date
20 June 2024
Reseach Article

Handwritten Devanagari Lipi using Support Vector Machine

by Shailendra Kumar Shrivastava, Pratibha Chaurasia
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 43 - Number 20
Year of Publication: 2012
Authors: Shailendra Kumar Shrivastava, Pratibha Chaurasia
10.5120/6220-8785

Shailendra Kumar Shrivastava, Pratibha Chaurasia . Handwritten Devanagari Lipi using Support Vector Machine. International Journal of Computer Applications. 43, 20 ( April 2012), 20-25. DOI=10.5120/6220-8785

@article{ 10.5120/6220-8785,
author = { Shailendra Kumar Shrivastava, Pratibha Chaurasia },
title = { Handwritten Devanagari Lipi using Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 43 },
number = { 20 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 20-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume43/number20/6220-8785/ },
doi = { 10.5120/6220-8785 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:33:55.047159+05:30
%A Shailendra Kumar Shrivastava
%A Pratibha Chaurasia
%T Handwritten Devanagari Lipi using Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 43
%N 20
%P 20-25
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The handwritten recognition is a one of the basic biometric recognition technique. Different technique and features are used for the faithful recognition characters. In this paper we have proposed a SVM (support vector machine) based technique for Devanagari character recognition. The Devanagari characters have very correlation to each other. This feature of the Devanagari lipi make difficult to faithful recognition. The energy features of segment characters are used for the classification. The more no. of segmentation improves the recognition rate. The different recognition rates with no. of segment are used in this paper. The recognition rate is also developed on the kernel of SVM. The result of different kernel is also given in this paper.

References
  1. Supriya Deshmukh, Leena Ragha "Analysis of Directional Features – Stroke and Contour for Handwritten Character Recognition"2009 IEEE International Advance Computing Conference (IACC 2009) Patiala, India, 6-7 March 2009.
  2. Anil kumar N. Holambe, Dr. Ravinder C. Thool "Comparative Study of Devanagari Handwritten and printed Character & Numerals Recognition using Nearest-Neighbor Classifiers"978-1-4244-5540- 9/10/ ©2010 IEEE.
  3. Shubhangi D. C. , P. S. Hiremath, "Multi-Class SVM Classifier for English Handwritten Digit Recognition using Manual Class Segmentation", Proc. Int'l Conf. on Advances in Computing. Communication and Control (ICAC3'09) 2009, pp. 353-356.
  4. Sabri A. Mahmoud and Sameh M. Awaida, "Recognition Of Off-Line Handwritten Arabic (Indian) Numerals Using Multi-Scale Features And Support Vector Machines Vs. Hidden Markov Models" The Arabian Journal For Science And Engineering, International Journal of Computer Applications (0975 – 8887) Volume 8– No. 9, October 2010 34, Number 2b, October , 2009, Pp. 430-444.
  5. A. Borji, and M. Hamidi, "Support Vector Machine for Persian Font Recognition", International Journal of Intelligent Systems and Technologies, Summer 2007, pp. 184-187.
  6. Miguel Po-Hsien Wu, "Handwritten Character Recognition" A thesis Report, University of Quinsland, October 29, 2003.
  7. Sandip Kaur, " Recognition of Handwritten Devanagri Script using Feature Based on Zernike Moments and Zoning and Neural Network Classifier", A M. Tech. Thesis Report, Panjabi University, Patiala, 2004, pp.
  8. Gaurav Jain, Jason Ko, "Handwritten Digits Recognition",Multimedia Systems, Project Report, University of Toronto,November 21, 2008, pp. 1-3.
  9. Shailedra Kumar Shrivastava, Sanjay S. Gharde "Support Vector Machine for Handwritten Devanagari Numeral Recognition" International Journal of Computer Applications (0975 – 8887) Volume 7– No. 11, October 2010.
  10. 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, ISSN: 0976–0466, Volume 1, Issue 2, 2010, pp-06-09.
  11. Vikas J Dongre,Vijay H Mankar " A Review of Research on Devnagari Character Recognition" International Journal of Computer Applications (0975 – 8887) Volume 12– No. 2, November 2010.
  12. J. Pradeep, E. Srinivasan, S. Himavathi "Diagonal Feature Extraction Based Handwritten Character System Using Neural Network" International Journal of Computer Applications (0975 – 8887) Volume 8– No. 9, October 2010.
  13. Minal Tomar and Pratibha Singh "A Directional Feature with Energy based Offline Signature Verification Network" International Journal on Soft Computing ( IJSC ), Vol. 2, No. 1, February 2011.
  14. G. G. Rajput, S. M. Mali, "Fourier Descriptor Based Isolated Marathi Handwritten Numeral Recognition", Int. Journal of Computer Application (0975 – 8887), Vol. 3, No. 4, 2010,pp. 9-13.
  15. Fabin Lauer, Ching Y. Suen, Gerard Bloch, "Trainable Feature Extractor for Handwritten Digit Recognition", Elsevier Science, 2 February, 2006.
Index Terms

Computer Science
Information Sciences

Keywords

Support Vector Machine Devnagiri Lipi Recognition Pre-processing Feature Extraction Classification Post Processing