Skip to main content

Recognition of Handwritten Indic Script Using Clonal Selection Algorithm

  • Conference paper
Artificial Immune Systems (ICARIS 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4163))

Included in the following conference series:

  • 675 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  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. 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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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. Watkins, A.B.: AIRS: a resource limited artificial immune classifier. Master’s dissertation, Dept. of Computer Science, Mississippi State University (2001)

    Google Scholar 

  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. 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. 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. 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. 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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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. 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. 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. 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. 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 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Garain, U., Chakraborty, M.P., Dasgupta, D. (2006). Recognition of Handwritten Indic Script Using Clonal Selection Algorithm. In: Bersini, H., Carneiro, J. (eds) Artificial Immune Systems. ICARIS 2006. Lecture Notes in Computer Science, vol 4163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11823940_20

Download citation

  • DOI: https://doi.org/10.1007/11823940_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37749-8

  • Online ISBN: 978-3-540-37751-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics