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A Neural Nework Approach to Printed Devanagari Character Recognition

by Surendra P. Ramteke, Ramesh D. Shelke, Nilima P. Patil
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 22
Year of Publication: 2013
Authors: Surendra P. Ramteke, Ramesh D. Shelke, Nilima P. Patil
10.5120/10230-4917

Surendra P. Ramteke, Ramesh D. Shelke, Nilima P. Patil . A Neural Nework Approach to Printed Devanagari Character Recognition. International Journal of Computer Applications. 61, 22 ( January 2013), 33-37. DOI=10.5120/10230-4917

@article{ 10.5120/10230-4917,
author = { Surendra P. Ramteke, Ramesh D. Shelke, Nilima P. Patil },
title = { A Neural Nework Approach to Printed Devanagari Character Recognition },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 22 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 33-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number22/10230-4917/ },
doi = { 10.5120/10230-4917 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:10:19.943016+05:30
%A Surendra P. Ramteke
%A Ramesh D. Shelke
%A Nilima P. Patil
%T A Neural Nework Approach to Printed Devanagari Character Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 22
%P 33-37
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper we deals with the recognition of printed Devanagari Characters with neural network approach. The paper shows measurement of the effectiveness classifier in terms of precision in recognition. It is also a benchmark for testing and verifying new pattern recognition theories and algorithms. 10 samples of each devanagari vowel and consonant from 10 different printed kruti dev font have been sampled and database was prepared. After segmentation, an individual image is normalized to 100X100 pixel size. Seven moment invariants (MIs) are evaluated for each character along with GLCM properties like Contrast, Homogeneity, Entropy, Correlation , color domain and histogram. The Neural network function has been adopted for classification. The main objective of the paper is to test the possibility of using the MI for recognition of printed character independent of its Size, slant and other variations.

References
  1. Chomtip Pornanomchai ,Dentcho N. Batanov and Nicholasl Dimmitt Recognizing Thai handwritten Character and words for human computer interaction", International Journal of Human- Computer Studies, pp. 259-279, (2001)
  2. Comparative Study Of Different Classifiers For Devanagari Handwritten Character Recognition Anilkumar Holambe et. al. / International Journal of Engineering Science and Technology Vol. 2 (7), 2010, 2681-2689
  3. T. Wakabayashi, M. Shi, W. Ohyama, and F. Kimura: "A Comparative Study on MirrorImage Learning and ALSM":In Proc. 8th
  4. M. Ikeda, H. Tanaka and t. Motooka,"Projection distance method for recognition of handwritten character (in japanese),"Trans. ISP Japan Vol. 24. no. 1,pp 106-112,1983
  5. Cohn, D. A. , Ghahramani, Z. , & Jordan, M. I. . Active learning with statistical models. Advances in Neura Information Processing Systems (pp. 705–712). The MIT Press (1995).
  6. M. Cheriet, N. Kharma, C. lin Liu, and C. Suen. Character Recognition Systems: A Guide for Students and Practitioners. Wiley-Interscience, 2007
  7. R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, second edition, Wiley Interscience, 2001.
  8. V. Vapnik. The Natureof Statistical Learning Theory. Springer,1995.
  9. Freeman, H. , On the Encoding of Arbitrary Geometric Configurations, IRE Trans. on Electr. Comp. or TC(10), No. 2, June, 1961, pp. 260-268
  10. E. R. Davies and A. P. Plummer,"Thinning Algorithms: A critique and new Methodology", Pattern Recognition 14,
  11. : 53-63
  12. S. Arora, D. Bhattacharjee, M. Nasipuri, L. Malik, "A Novel Approach for Handwritten Devnagari Character Recognition", International Conference on Signal and Image Processing (ICSIP), Hubli, Karnataka, India, 2006
  13. R J Ramteke Invariant Moments Based Feature Extraction for Handwritten Devanagari Vowels Recognition International Journal of Computer Applications (0975 - 8887) Volume 1 – No. 18
  14. An Overview of Character Recognition Focused on Off-Line Handwriting, ieee transactions on systems, man, and cybernetics—part c: applications and reviews, vol. 31, no. 2, may 2001
  15. Artificial Neural Network Based Character Recognition Using Backpropagat, International Journal of Computers & Technology ISSN: 2277-3061 Volume 3, No. 1, AUG, 2012
  16. Statistical Texture Measures Computed from Gray Level Concurrence Matrices, Fritz Albregtsen Image Processing Laboratory Department of Informatics University of Oslo November 5, 2008
  17. Printed Arabic characters classification using a statistical approach, International Journal of Computers and Technology ISSN: 2277-3061 Volume 3. No. 1, AUG, 2 012
Index Terms

Computer Science
Information Sciences

Keywords

Histogram Moment Invariant GLCM color domain ANN

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