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Handwritten Malayalam Character Recognition using Curvelet Transform and ANN

by Manju Manuel, Saidas S. R
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 121 - Number 6
Year of Publication: 2015
Authors: Manju Manuel, Saidas S. R
10.5120/21544-4559

Manju Manuel, Saidas S. R . Handwritten Malayalam Character Recognition using Curvelet Transform and ANN. International Journal of Computer Applications. 121, 6 ( July 2015), 24-31. DOI=10.5120/21544-4559

@article{ 10.5120/21544-4559,
author = { Manju Manuel, Saidas S. R },
title = { Handwritten Malayalam Character Recognition using Curvelet Transform and ANN },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 121 },
number = { 6 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 24-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume121/number6/21544-4559/ },
doi = { 10.5120/21544-4559 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:07:43.956401+05:30
%A Manju Manuel
%A Saidas S. R
%T Handwritten Malayalam Character Recognition using Curvelet Transform and ANN
%J International Journal of Computer Applications
%@ 0975-8887
%V 121
%N 6
%P 24-31
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Malayalam, the official language of Kerala, a southern state of India has been accorded the honour of language of eminence. Hence the researches in recognition and related works in Malayalam language is gaining more prominence in the current scenario. This paper proposes the use of Curvelet transform and neural network for the recognition of handwritten Malayalam character. Curvelet transform is to be used in the feature extraction stage and neural network for classification. Curvelet transform provides a compact representation for curved singularities and is well suited for malayalam language. Two different back propagation algorithms had been employed and the performance is compared on varying architecture. The promising feature of the work is successful classification of 53 characters which is an improvement over the existing works. Application of character recognition include sorting of bank cheques and postal letters, reading aid for blind, data compression etc. Besides, an automated tool with graphical user interface in MATLAB has been developed for Malayalam character recognition.

References
  1. Malayalam Script Features [Online]. Available: http://scriptsource. org/scr/Mlym
  2. Malayalam [Online]. Available: http://www. omniglot. com/writing/malayalam. htm
  3. L. Eikvil, "OCR - optical character recognition," 1993.
  4. G. Raju, "Recognition of unconstrained handwritten malayalam characters using zero-crossing of wavelet coefficients," Advanced Computing and ommunications, pp. 217–221, 2006.
  5. B. Philip and R. S. Samuel, "A novel bilingual ocr for printed malayalam-english text based on gabor features and dominant singular values," in Proceedings of the International Conference on Digital Image Processing, 2009, pp. 361–365.
  6. R. John, G. Raju, and D. S. Guru, "1d wavelet transform of projection profiles for isolated handwritten malayalam character recognition," International Conference on Computational Intelligence and Multimedia Applications, pp. 481–485, 2007.
  7. M. A. Rahiman and M. S. Rajasree, "Printed malayalam character recognition using back-propagation neural networks," IEEE International Advance Computing Conference, pp. 197–201, 2009.
  8. B. Philip and R. D. S. Samuel, "An efficient ocr for printed Malayalam text using novel segmentation algorithm and svm classifiers," ACADEMYPUBLISHER, vol. 1, pp. 179–182, May 2009.
  9. A. Rahiman and M. S. Rajasree, "Recognition of handwritten Malayalam characters using vertical & horizontal line positional analyzer algorithm," ICMLC, vol. 4, pp. 404–410, 2011.
  10. A. T. Jia, A. Yahkoob, and S. K, "Malayalam ocr: N-gram approach using svm classifier," International Conference on Advances in Computing,Communications and Informatics (ICACCI), pp. 1799–1803, 2013.
  11. J. John, P. K. V. , and K. Balakrishnan, "Offline handwritten Malayalam character recognition based on chain code histogram," pp. 736–741,2011.
  12. [Online]. Available: http://www. curvelet. org/
  13. E. Candes, L. Demanet, D. Donoho, and L. Ying, "Fast discrete curvelettransforms," MultiscaleModeling& Simulation, vol. 5, no. 3, pp. 861–899, 2006.
  14. T. Guha and Q. J. Wu, "Curvelet based feature extraction," FaceRecognition, pp. 35–46, 2010.
  15. S. Sivanandam and S. Deepa, Introduction to Neural Networks UsingMatlab 6. 0, ser. Computer engineering series. Tata McGraw-Hill, 2006. [Online]. Available: http://books. google. co. in/books?id=jJTN8RPgyXgC
  16. M. Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems. Addison-Wesley, 2005. [Online]. Available: http://books. google. co. in/books?id=1BxYQnrfv9MC
  17. R. Vilaithong, S. Tenbohlen, and T. Stirl, "Neural network for transformer top-oil temperature prediction. "Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
Index Terms

Computer Science
Information Sciences

Keywords

Malayalam Character Recognition Artificial Neural Network (ANN) Curvelet Transform Handwritten.

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