Abstract
Abstraction: The work explores the potentiality of a clonal selection algorithm and it's hybridizing with the genetic algorithm GA in cursive and discrete handwritten English character recognition. In particular, a retraining scheme for the clonal selection algorithm is formulated for better recognition rates. Empirical study with a dataset (which contains about 100 handwritten samples for 26 characters taken from 30 persons) shows that the proposed approach exhibits very good generalization ability, such that results reported recognition accuracy reached to 100% for the recognition of characters that have been used in building database, and an average recognition accuracy of about 94% for other characters.
