CFP last date
20 June 2024
Reseach Article

Article:A Study on the Effect of Outliers in Devanagari Character Recognition

by O.V. Ramana Murthy, M. Hanmandlu
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 32 - Number 10
Year of Publication: 2011
Authors: O.V. Ramana Murthy, M. Hanmandlu
10.5120/3937-5183

O.V. Ramana Murthy, M. Hanmandlu . Article:A Study on the Effect of Outliers in Devanagari Character Recognition. International Journal of Computer Applications. 32, 10 ( October 2011), 10-17. DOI=10.5120/3937-5183

@article{ 10.5120/3937-5183,
author = { O.V. Ramana Murthy, M. Hanmandlu },
title = { Article:A Study on the Effect of Outliers in Devanagari Character Recognition },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 32 },
number = { 10 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 10-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume32/number10/3937-5183/ },
doi = { 10.5120/3937-5183 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:18:55.215348+05:30
%A O.V. Ramana Murthy
%A M. Hanmandlu
%T Article:A Study on the Effect of Outliers in Devanagari Character Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 32
%N 10
%P 10-17
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Devanagari is the basic script for many languages of India, including their National language Hindi. Unlike the Latin script used for the English language, it does not have upper case or lowercase. It has only one case of writing. Moreover each alphabet contains more curves than straight lines. Hence handwritten Devanagari character recognition is a challenging task. To capture different handwritten styles of each alphabet, different approaches have been proposed. In this work, we investigate a simple filtering technique on the features. Support Vector Machine (SVM) was used as classifier. It has been applied on two benchmark Devanagari databases and results show an improvement of as much as 5-10%. This improvement is found to be consistent with different sizes of the database. It was studied on pixel density features and GIST features separately. GIST features were found to be more effective and like having the potency of self-containing filtering.

References
  1. V. Barnett and T. Lewis , “Outliers in Statistical Data”, Wiley Series in Probability and Mathematical Statistics, John Wiley & Sons, Chichester, 1994.
  2. Mohamed Cheriet, Nawwaf Kharma, Cheng-Lin Liu, Ching Y. Suen, “Character Recognition Systems: A Guide for students and Practioners”, John Wiley & Sons, Inc., Hoboken, New Jersey, 2007.
  3. Chih-Chung Chang and Chih-Jen Lin, “ LIBSVM a library for support vector machines”, ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011.
  4. Vikas J Dongre and Vijay H Mankar, “A Review of Research on Devanagari Character Recognition”, International Journal of Computer Applications, 12(2):8–15, December 2010.
  5. F.E. Grubbs, "Procedures for Detecting Outlying Observations in Samples”, Technometrics, 11-1, pp.1--21; Feb., 1969
  6. M. Bokser, “Omnidocument technologies,” Proc. IEEE, vol. 80, no. 7, pp. 1066–1078, Jul. 1992.
  7. M. Hanmandlu, O. V. Ramana Murthy, Vamsi Krishna Madasu, “Fuzzy Model based recognition of handwritten Hindi characters”, Digital Image Computing Techniques and Applications, DICTA 3-5 Dec 2007, pp. 454-461.
  8. U. Pal, B. B. Chaudhuri, ''Indian Script Character recognition: A survey'', Pattern Recognition, vol. 37, pp. 1887-1899, 2004.
  9. U. Pal, N. Sharma, T. Wakabayashi and F. Kimura, "Off-Line Handwritten Character Recognition of Devanagari Script", In Proceedings 9th International Conference on Document Analysis and Recognition. Pp. 496-500, Curitiba, Brazil, September 24-26, 2007.
  10. U. Pal, T. Wakabayashi and F. Kimura, "Comparative Study of Devnagari Handwritten Character Recognition using Different Feature and Classifiers", In Proc. 10th International Conference on Document Analysis and Recognition (ICDAR), pp.1111-1115, 2009
  11. N. Sharma, U. Pal, F. Kimura and S. Pal, “Recognition of Offline Handwritten Devanagari Characters using Quadratic Classifier”, In Proc. Indian Conference on Computer Vision Graphics and Image Processing, pp- 805-816, 2006
  12. V. Vapnik, “The Nature of Statistical Learning Theory”, Springer Verlag, 1995.
  13. Stigler S, "Fisher and the 5% level". Chance, 2008, 21 (4): 12.
  14. Fisher R. A., Statistical Methods for Research Workers (first ed.). Edinburgh: Oliver & Boyd, 1925
Index Terms

Computer Science
Information Sciences

Keywords

Outliers Support Vector Machine Character recognition pixel density features GIST features