Recognize Handwritten Urdu Script Using Kohenen Som Algorithm

  • Khan, Yunus ;
  • Nagar, Chetan
  • Received : 2011.12.20
  • Accepted : 2012.02.27
  • Published : 2012.02.29


In this paper we use the Kohonen neural network based Self Organizing Map (SOM) algorithm for Urdu Character Recognition. Kohenen NN have more efficient in terms of performance as compare to other approaches. Classification is used to recognize hand written Urdu character. The number of possible unknown character is reducing by pre-classification with respect to subset of the total character set. So the proposed algorithm is attempt to group similar character. Members of pre-classified group are further analyzed using a statistical classifier for final recognition. A recognition rate of around 79.9% was achieved for the first choice and more than 98.5% for the top three choices. The result of this paper shows that the proposed Kohonen SOM algorithm yields promising output and feasible with other existing techniques.


Pre-Classification;Neural Network;Handwritten character;SOM;Baseline;Statistical;Structural;Crux;Meticulous and Sobel edge detection


  1. C. E. Dunn and P. S. P. Wang, "Character segmentation techniques for handwritten text - a survey", in the Proceedings of 11th ICPR, Vol. 2, pp. 577-580, 1992.
  2. R. M. Bozinovic and S. N. Srihari, "Off-line cursive script word recognition", IEEE Trans. on Pattern Anal. Mach. Intell., vol. 11, no. 1, pp. 68-83, Jan. 1989.
  3. Hu, M. K. Brown and W. Turin, "HMM based on-line handwriting recognition", IEEE Trans. on pattern Anal. Mach. Intell., vol. 18, no. 10, pp. 1039-1045, Oct. 1996.
  4. U. Pal and B. B. Chaudhuri, "Indian script character recognition: a survey", Pattern Recognition, Vol. 37(9), pp. 1887-1899.
  5. _of _ India Tentative System
  6. D. Deng, K. P. Chan, and Y. Yu, "Handwrit-ten Chinese character recognition using spatial Gabor filters and selforganizing feature maps", Proc. IEEE Inter. Confer. On Image Processing, vol. 3, pp. 940-944, Austin TX, June 1994.
  7. C-H. Chang, "Simulated annealing clustering of Chinese words for contextual text recognition", Pattern Recognition Letters, vol. 17, no. 1, pp. 57-66, 1996.
  8. H. Yamada, K. Yamamoto, and T. Saito, "A non-linear normalization method for handprinted Kanji character recognition-line density equalization", Pattern Recognition, vol. 23, no. 9, pp. 1023-1029, 1990.
  9. S. D. Connell, R. M. K. Sinha and A. K. Jain, "Recognition of unconstrained On-line Devanagari characters", in the Proceedings of 15 International Conference on Pattern Recogni-tion (ICPR), Vol. 2, Spain, pp. 368-371, 2000.
  10. S. D. Connell and A. K. Jain, "Template-based online character recognition", Pattern Recognition , Vol. 34(1), pp. 1-14, 2001.
  11. Bangla A. K. Ray and B. Chatterjee, "Design of a nearest neighbor classifier system for Bengali character recognition", J. Inst. Elec. Telecom. Eng., Vol. 30, pp. 226-229, 1984.
  12. S. N. S Rajasekaran and B. L. Deekshatulu, "Recognition of printed Telugu characters", Computer Graphics and Image Processing (CGIP), Vol. 6, pp. 335- 360, 1977.
  13. C. V. Lakshmi and C. Patvardhan, "A high accuracy OCR system for printed Telugu text", in the Proceedings of Conference on Convergent Technologies for Asia-Pacific Region (TENCON 2003), Vol. 2, pp. 725-729, 2003
  14. A. Negi, C. Bhagvati and B. Krishna, "An OCR system for Telugu", in the Proceedings of the Sixth International Conference on Document Processing, pp. 1110-1114, 2001.