Software engineering of a clonal selection algorithm and it's hybridizing with the genetic algorithm GA in cursive and discrete handwritten English character recognition.
Abstraction:<br /> 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 f...
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College of Education for Pure Sciences
2013-08-01
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Online Access: | https://edusj.mosuljournals.com/article_89897_7eceddb7adfa4d574bb32a798372d118.pdf |
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doaj-aeda9f6056f64a59add0c10b5fea48732020-11-24T21:22:39ZaraCollege of Education for Pure Sciencesمجلة التربية والعلم1812-125X2664-25302013-08-0126315217810.33899/edusj.2013.8989789897Software engineering of a clonal selection algorithm and it's hybridizing with the genetic algorithm GA in cursive and discrete handwritten English character recognition.Shereen Moataz Muhammad SiddiqJamal Salahddin Sayed MajeedAbstraction:<br /> 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.https://edusj.mosuljournals.com/article_89897_7eceddb7adfa4d574bb32a798372d118.pdfsoftware engineeringclonal selection algorithmhybridizinggenetic algorithm ga |
collection |
DOAJ |
language |
Arabic |
format |
Article |
sources |
DOAJ |
author |
Shereen Moataz Muhammad Siddiq Jamal Salahddin Sayed Majeed |
spellingShingle |
Shereen Moataz Muhammad Siddiq Jamal Salahddin Sayed Majeed Software engineering of a clonal selection algorithm and it's hybridizing with the genetic algorithm GA in cursive and discrete handwritten English character recognition. مجلة التربية والعلم software engineering clonal selection algorithm hybridizing genetic algorithm ga |
author_facet |
Shereen Moataz Muhammad Siddiq Jamal Salahddin Sayed Majeed |
author_sort |
Shereen Moataz Muhammad Siddiq |
title |
Software engineering of a clonal selection algorithm and it's hybridizing with the genetic algorithm GA in cursive and discrete handwritten English character recognition. |
title_short |
Software engineering of a clonal selection algorithm and it's hybridizing with the genetic algorithm GA in cursive and discrete handwritten English character recognition. |
title_full |
Software engineering of a clonal selection algorithm and it's hybridizing with the genetic algorithm GA in cursive and discrete handwritten English character recognition. |
title_fullStr |
Software engineering of a clonal selection algorithm and it's hybridizing with the genetic algorithm GA in cursive and discrete handwritten English character recognition. |
title_full_unstemmed |
Software engineering of a clonal selection algorithm and it's hybridizing with the genetic algorithm GA in cursive and discrete handwritten English character recognition. |
title_sort |
software engineering of a clonal selection algorithm and it's hybridizing with the genetic algorithm ga in cursive and discrete handwritten english character recognition. |
publisher |
College of Education for Pure Sciences |
series |
مجلة التربية والعلم |
issn |
1812-125X 2664-2530 |
publishDate |
2013-08-01 |
description |
Abstraction:<br /> 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. |
topic |
software engineering clonal selection algorithm hybridizing genetic algorithm ga |
url |
https://edusj.mosuljournals.com/article_89897_7eceddb7adfa4d574bb32a798372d118.pdf |
work_keys_str_mv |
AT shereenmoatazmuhammadsiddiq softwareengineeringofaclonalselectionalgorithmanditshybridizingwiththegeneticalgorithmgaincursiveanddiscretehandwrittenenglishcharacterrecognition AT jamalsalahddinsayedmajeed softwareengineeringofaclonalselectionalgorithmanditshybridizingwiththegeneticalgorithmgaincursiveanddiscretehandwrittenenglishcharacterrecognition |
_version_ |
1725994724108533760 |