Abstract
ion: 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
