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.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
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)
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)
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)
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)
Watkins, A.B.: AIRS: a resource limited artificial immune classifier. Master’s dissertation, Dept. of Computer Science, Mississippi State University (2001)
Keith Price Bibliography on use of Neural Networks for recognition of Numbers and Digits at, http://iris.usc.edu/Vision-Notes/bibliography/char1019.html
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)
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)
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)
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)
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)
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)
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)
Timmis, J.: Artificial Immune Systems: a novel data analysis techniques inspired by the immune network theory. Ph.D Thesis, University of Wales, Aberystwyth (2001)
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)
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/
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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)