Title

Recognition of handwritten indic script using clonal selection algorithm

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. © Springer-Verlag Berlin Heidelberg 2006.

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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