CFP last date
20 June 2024
Reseach Article

Online Handwriting Recognition of Gurmukhi and Devanagiri Characters in Mobile Phone Devices

Published on April 2012 by Anuj Sharma, Kalpana Dahiya
International Conference on Recent Advances and Future Trends in Information Technology (iRAFIT 2012)
Foundation of Computer Science USA
IRAFIT - Number 1
April 2012
Authors: Anuj Sharma, Kalpana Dahiya
35a8ef1e-3e74-4ab7-b5db-4f20332dc1ad

Anuj Sharma, Kalpana Dahiya . Online Handwriting Recognition of Gurmukhi and Devanagiri Characters in Mobile Phone Devices. International Conference on Recent Advances and Future Trends in Information Technology (iRAFIT 2012). IRAFIT, 1 (April 2012), 37-41.

@article{
author = { Anuj Sharma, Kalpana Dahiya },
title = { Online Handwriting Recognition of Gurmukhi and Devanagiri Characters in Mobile Phone Devices },
journal = { International Conference on Recent Advances and Future Trends in Information Technology (iRAFIT 2012) },
issue_date = { April 2012 },
volume = { IRAFIT },
number = { 1 },
month = { April },
year = { 2012 },
issn = 0975-8887,
pages = { 37-41 },
numpages = 5,
url = { /proceedings/irafit/number1/5852-1008/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Recent Advances and Future Trends in Information Technology (iRAFIT 2012)
%A Anuj Sharma
%A Kalpana Dahiya
%T Online Handwriting Recognition of Gurmukhi and Devanagiri Characters in Mobile Phone Devices
%J International Conference on Recent Advances and Future Trends in Information Technology (iRAFIT 2012)
%@ 0975-8887
%V IRAFIT
%N 1
%P 37-41
%D 2012
%I International Journal of Computer Applications
Abstract

This paper presents a system to recognize online handwritten Gurmukhi and Devanagiri characters in touch screen based mobile phones. We have used small line segments (derived from elastic matching and chain code techniques) to recognize Gurmukhi and Devanagiri characters. Mobile phones offer main challenges as: less memory and slow processer speed in comparison to Desktop or notebooks or Tablet PCs. We have proposed a system to work effectively in mobile phones in view to see memory and processor limitations. In Gurmukhi, we have achieved an overall recognition rate as 94.69% for a training set of 5330 characters and test set include 1640 characters; the recognition rate for Devanagiri characters for training set of 1050 characters and test set of 504 characters is 86.90%.

References
  1. Sharma, A., Sharma, R. K. and Kumar, R. 2009. Online Preprocessing of Handwritten Gurmukhi Strokes, International Journal of Machine Graphics and Vision, Vol. 18, no. 1, pp. 105-120.
  2. Sharma, A., Kumar, R. and Sharma, R. K. 2008. Recognizing Online Handwritten Gurmukhi Characters using Elastic Matching. IEEE proceedings of International Congress on Image and Signal Processing (CISP-2008), Sanya, vol. 2, pp. 391-396.
  3. Sharma, A., Kumar, R. and Sharma, R. K. 2009. Rearrangement of Strokes in Recognition of Online Handwritten Gurmukhi Words. In IEEE Proceedings of 10th International Conference on Document Analysis and Recognition, Barcelona, Spain (ICDAR-2009), pp. 1241-1245.
  4. Sharma, D.V and Lehal, G.S. 2006. An Iterative Algorithm for Segmentation of Isolated Handwritten Words in Gurmukhi Script. IEEE proceedings of ICPR.
  5. Sachan, M.K., Lehal, G.S. and Jain, V.K. 2011. A Novel Method to Segment Online Gurmukhi Script. Information Systems for Indian Languages, Communications in Computer and Information Science, Vol. 139, Springer-Verlag, pp. 1-8.
  6. Joshi, N., Sita, G., Ramakrishnan, A. G., Deepu, V., and Madhvanath, S. Machine recognition of online handwritten Devanagari characters. Proceedings of International Conference of Document Analysis and Recognition, pp. 1156-1160.
  7. Bhattacharya, U., Purui, S. K., Shaw, B. and Bhattacharya, K. 2006. Neural combination of ANN and HMM for handwritten Devanagari numeral recognition. Proceedings of ICFHR, pp. 613-618.
  8. Swethalakshmi, H., Jayaraman, A., Chakarvarthy, V. S. and Sekhar. C. C. Online handwritten character recognition of Devanagari and Telugu characters using support vector machines. Proceedings of IWFHR.
  9. Bhattacharya, U., Gupta, B. K., Parui, S., Direction code based features for recognition of online handwritten characters of Bangla. Proceedings of International Conference on Document Analysis and Recognition, vol. 1, pp.58-62.
  10. Babu, V., Prasanth, L., Sharma, R., Rao, G. V., and Bharath, A., HMM-Based online handwriting recognition system for Telugu symbols. Proceedings of the ninth International Conference on Document Analysis and Recognition, vol.1, pp. 63-67.
  11. Prasanth, L., Babu, V., Sharma, R., Rao, G. V., and M., D., Elastic matching of online handwritten tamil and telugu scripts using local features. Proceedings of the ninth International Conference on Document Analysis and Recognition, vol. 2, pp. 1028-1032.
  12. Jain, A.K, Robert, P.W and Mao, J. Statistical Pattern Recognition – A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, pp. 4-37.
  13. Sharma, A., Kumar, R. and Sharma, R. K. Recognizing Online Handwritten Gurmukhi Characters using Comparison of Small Line Segments. International Journal of Computer Theory and Engineering, A Journal of World Academy of Computer Science and Information Technology, vol. 1, no. 2, pp. 136-141.
  14. Sharma, A. 2009. Online Handwritten Gurmukhi Character Recognition. PhD Thesis, Thapar University, Patiala.
  15. Beigi, H., Nathan, K., Clary, G. J., and Subhramonia, J. Size normalization in unconstrained online handwritng recognition. Proceedings ICIP, 169-173
  16. Unser, M., Aldroubi, A., Eden, M. 1993. B-Spline signal processing: part II - efficient design and applications. IEEE Transactions on Signal Processing, 41(2), 834-848.
  17. Kavallieratou, E., Fakatakis, N., Kolkkinakis, G. An unconstrained handwriting recognition system. International Journal of Document Analysis and Recognition, 4(4), 226-242.
  18. Brault, J. J. and Plamondon, R. Segmenting handwritten signatures at their perceptually important points. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9), 953-957.
  19. Guerfali, W. and Plamondon, R. Normalizing and restoring online handwriting. Pattern Recognition, 26(3), 419.
  20. Bellegarda, E. J., Bellegarda, J. R., Namahoo, D. and Nathan, K. S. A probabilistic framework for online handwriting recognition. Proceedings of IWFHR III, pp. 225-234.
  21. Jain, A.K and Dubes, R.C. Algorithms for Clustering Data. Prentice Hall.
Index Terms

Computer Science
Information Sciences

Keywords

Online Handwriting Recognition Preprocessing Elastic Matching Chain Code Technique Post-processing