Offline Handwritten Malayalam Word Recognition using Wavelet Transform

Authors(2) :-Jino P J, Kannan Balakrishnan

Wavelet transforms of malaylam handwritten images are used for the recogntion. A comparative study with Haar, Daubechies wavelets are also performed. Lexicon contains fourteen district names and a total of 736 samples.More than 90 % of recogntion is achieved.Low Frequency components are considered as features. For the classification SVM used with RBF kernel.The Dimensionality of the wavelet coefficients are reduced by Principal Component Analysis(PCA).

Authors and Affiliations

Jino P J
Artificial Intelligence Lab,Department of Computer Applications, Cochin University, Kerala, India
Kannan Balakrishnan
Department of Computer Applications , Cochin University,Kerala ,India

Offline Handwritten Recognition, Wavelet Transform ,Feature Extraction method, Pattern Recognition.

  1. Neeba, NV and Namboodiri, Anoop and Jawahar, CV and Narayanan, PJ.(2009) ‘Recognition of Malayalam documents’, Springer, pp.125-146.
  2. Mukhopadhyay, S., Das, N. K., Pradhan, A., Ghosh, N., and Panigrahi, P. K. (2014) ’Wavelet and multi-fractal based analysis on DIC images in epithelium region to detect and diagnose the cancer progress among different grades of tissues’, SPIE Photonics Europe,pp.91290Z-91290,International Society for Optics and Photonics
  3. Qureshi, Muhammad Ali, and Mohamed Deriche. (2016) ’A new wavelet based efficient image compression algorithm using compressive sensing’, Multimedia Tools and Applications,Vol. 75, No. 12, pp.6737-6754
  4. Elshazly, Ehab H and Faragallah, Osama S and Abbas, Alaa M and Ashour, Mahmoud A and El-Rabaie, El-SayedMand Kazemian, Hassan and Alshebeili, Saleh Aand El-Samie, Fathi E Abd and El-sayed, Hala S. (2015) ’Robust and secure fractional wavelet image watermarking’, Signal, Image and Video Processing, Vol. 9, No. 1, pp.89-98, Springer
  5. Rasti, Pejman and Lüsi, Iiris and Demirel, Hasan and Kiefer, Rudolf and Anbarjafari,Gholamreza. 2014 ’Wavelet transform based new interpolation technique for satellite image resolution enhancement’, Aerospace Electronics and Remote Sensing Technology (ICARES), 2014 IEEE International Conference on,pp.185-188,IEEE.
  6. Abdulrahman, Muzammil and Gwadabe, Tajuddeen R and Abdu, Fahad J and Eleyan, Alaa (2014) ‘IEEE’, Gabor wavelet transform based facial expression recognition using PCA and LBP,pp.2265-2268.
  7. Liu, Cheng-Lin and Sako, Hiroshi and Fujisawa, Hiromichi. (2002) ‘Performance evaluation of pattern classifiers for handwritten character recognition’, International Journal on Document Analysis and Recognition, Vol. 4, No. 3, pp.191-204.
  8. LeCun, Yann and Jackel, LD and Bottou, Leon and Brunot, A and Cortes, Corinna and Denker, JS and Drucker, Harris and Guyon, I and Muller, UA and Sackinger, Eduard and others. (1995) ‘Comparison of learning algorithms for handwritten digit recognition’,International conference on artificial neural networks, Vol. 60, pp.53-60.
  9. Bhattacharya, Ujjwal and Chaudhuri, Bidyut Baran. (2009) ‘Handwritten numeral databases of Indian scripts and multistage recognition of mixed numerals’, IEEE transactions on pattern analysis and machine intelligence, Vol. 31, No. 3, pp.444-457.
  10. Chacko, Binu P and Krishnan,VRVimal and Raju,Gand Anto, P Babu.(2012) ‘Handwritten character recognition using wavelet energy and extreme learning machine’, International         Journal of Machine Learning and Cybernetics, Vol. 3, No. 2, pp.149-161.
  11. Jomy John and Pramod KV and Kannan Balakrishnan.(2012) ‘Unconstrained handwritten Malayalam character recognition using wavelet transform and support vector machine classifier’, Procedia Engineering,Elsevier, Vol. 30,pp.598-605.
  12. Raju G.(2006) ‘Recognition of unconstrained handwritten Malayalam characters using zero-crossing of wavelet coefficients’, International Conference on Advanced Computing and Communications,ADCOM 2006,IEEE,pp.217-221.
  13. Wunsch, Patrick and Laine, Andrew F.(1995) ‘Wavelet descriptors for multiresolution recognition of handprinted characters’, Pattern Recognition, Vol. 28, No. 8, pp.1237-1249.
  14. Shelke, Sushama, and Shaila Apte. "Multistage handwritten marathi compound character recognition using neural networks." Journal of Pattern Recognition Research 2.253-268 (2011).
  15. Mozaffari, Saeed, Karim Faez, and Hamidreza Rashidy Kanan. "Feature comparison between fractal codes and wavelet transform in handwritten alphanumeric recognition using SVM classifier." Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on. Vol. 2. IEEE, 2004.
  16. Patel, Dileep Kumar, et al. "Handwritten character recognition using multiresolution technique and euclidean distance metric." Journal of Signal and Information Processing 3.02 (2012): 208.
  17. Pasha, Saleem, and M. C. Padma. "Handwritten Kannada character recognition using wavelet transform and structural features." Emerging Research in Electronics, Computer Science and Technology (ICERECT), 2015 International Conference on. IEEE, 2015.
  18. Antonini, Marc, et al. "Image coding using wavelet transform." IEEE Transactions on image processing 1.2 (1992): 205-220.
  19. Mallat, Stephane G.(1989) ‘A theory for multiresolution signal decomposition: the wavelet representation’, IEEE transactions on pattern analysis and machine intelligence,IEEE,pp.674-693.
  20. Daubechies, Ingrid(1992) ‘Ten lectures on wavelets’, SIAM.
  21. Hsu, Chih-Wei and Chang, Chih-Chung and Lin, Chih-Jen and others.(2003) ‘A practical guide to support vector classification’, Pattern Recognition, pp.1-16.

Publication Details

Published in : Volume 2 | Issue 5 | September-October 2017
Date of Publication : 2017-10-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 948-954
Manuscript Number : CSEIT1725221
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Jino P J, Kannan Balakrishnan, "Offline Handwritten Malayalam Word Recognition using Wavelet Transform", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 5, pp.948-954, September-October-2017.
Journal URL : http://ijsrcseit.com/CSEIT1725221

Article Preview