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Recognition of Handwritten Indic Script Using Clonal Selection Algorithm

  • Utpal Garain
  • Mangal P. Chakraborty
  • Dipankar Dasgupta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4163)

Abstract

The work explores the potentiality of a clonal selection algorithm in pattern recognition (PR). In particular, a retraining scheme for the clonal selection algorithm is formulated for better recognition of handwritten numerals (a 10-class classification problem). Empirical study with two datasets (each of which contains about 12,000 handwritten samples for 10 numerals) shows that the proposed approach exhibits very good generalization ability. Experimental results reported the average recognition accuracy of about 96%. The effect of control parameters on the performance of the algorithm is analyzed and the scope for further improvement in recognition accuracy is discussed.

Keywords

Clonal selection algorithm character recognition Indic scripts handwritten digits 

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References

  1. 1.
    Dasgupta, D., Ji, Z., Gonzalez, F.: Artificial immune system (AIS) research in the last five years. In: Congress on Evolutionary Computation (CEC 2003), vol. 1, pp. 123–130 (2003) Google Scholar
  2. 2.
    Tang, Z., Tashima, K., Cao, Q.P.: Pattern recognition system using a clonal selection-based immune network. Systems and Computers in Japan 34(12), 56–63 (2003)CrossRefGoogle Scholar
  3. 3.
    Ji, Z., Dasgupta, D.: Real-Valued Negative Selection Algorithm with Variable-Sized Detectors. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 287–298. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    de Castro, L.N., Zuben, F.J.V.: Learning and Optimization Using the Clonal Selection Principle. IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems 6, 239–251 (2002)Google Scholar
  5. 5.
    Garain, U., Chakraborty, M.P., Dutta Majumder, D.: Improvement of OCR Accuracy by Similar Character Pair Discrimination: an Approach based on Artificial Immune System. In: The 18th Int. Conf. on Pattern Recognition (ICPR), Hongkong (August 2006)Google Scholar
  6. 6.
    Watkins, A.B.: AIRS: a resource limited artificial immune classifier. Master’s dissertation, Dept. of Computer Science, Mississippi State University (2001)Google Scholar
  7. 7.
    Keith Price Bibliography on use of Neural Networks for recognition of Numbers and Digits at, http://iris.usc.edu/Vision-Notes/bibliography/char1019.html
  8. 8.
    de Stefano, C., Della Cioppa, A., Marcelli, A.: Handwritten Numeral Recognition by Means of Evolutionary Algorithms. In: Proc. of the 5th Int. Conf. on Document Analysis and Recognition (ICDAR), Bangalore, India, pp. 804–808 (1999)Google Scholar
  9. 9.
    Carter, J.H.: The Immune System as a model for Pattern Recognition and classification. Journal of the American Medical Informatics Association 7(3), 28–41 (2000)Google Scholar
  10. 10.
    de Castro, L.N., Timmis, J.: Artificial Immune Systems: A Novel Approach to Pattern Recognition. In: Alonso, L., Corchado, J., Fyfe, C. (eds.) Artificial Neural Networks in Pattern Recognition, pp. 67–84. University of Paisley (January 2002)Google Scholar
  11. 11.
    Forrest, S., Javornik, B., Smith, R.E., Perelson, A.S.: Using genetic algorithms to explore pattern recognition in the immune system. Evolutionary Computation 1(3), 191–211 (1993)CrossRefGoogle Scholar
  12. 12.
    White, J.A., Garrett, S.M.: Improved Pattern Recognition with Artificial Clonal Selection? In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 181–193. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Cao, Y., Dasgupta, D.: An Immunogenetic Approach in Chemical Spectrum Recognition. In: Ghosh, Tsutsui (eds.) Advances in Evolutionary Computing, ch. 36. Springer-Verlag, Heidelberg (2003)Google Scholar
  14. 14.
    Tarakanov, Skormin, V.: Pattern Recognition by Immunocomputing. In: The Proceedings of the special sessions on artificial immune systems in Congress on Evolutionary Computation. In: 2002 IEEE World Congress on Computational Intelligence, Honolulu, Hawaii (May 2002)Google Scholar
  15. 15.
    Timmis, J.: Artificial Immune Systems: a novel data analysis techniques inspired by the immune network theory. Ph.D Thesis, University of Wales, Aberystwyth (2001)Google Scholar
  16. 16.
    Bhattacharya, U., Chaudhuri, B.B.: Databases for research on recognition of handwritten characters of Indian scripts. In: Proc. of the 8th Int. Conf. on Document Analysis and Recognition (ICDAR), Seoul, Korea, vol. II, pp. 789–793 (2005)Google Scholar
  17. 17.
    Hanmandlu, M., Ramana Murthy, O.V.: Fuzzy Model Based Recognition of Handwritten Hindi Numerals. In: Proc. Int. Conf. on Cognition and Recognition, December 2005, pp. 490–496 (2005), http://www.studentprogress.com/appln/colleges/cogrec/
  18. 18.
    Bhattacharya, U., Das, T.K., Dutta, A., Parui, S.K., Chaudhuri, B.B.: A Hybrid scheme for handwritten numeral recognition based on Self Organizing Network and MLP. In: Int. J. on Pattern Recognition and Artificial Intelligence (IJPRAI), vol. 16, pp. 845–864 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Utpal Garain
    • 1
  • Mangal P. Chakraborty
    • 1
  • Dipankar Dasgupta
    • 2
  1. 1.Indian Statistical InstituteKolkataIndia
  2. 2.The University of MemphisMemphis

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