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Reseach Article

Optical Character Recognition based on Genetic Algorithms and Machine Learning

by Arafat A. Muharram, Khaled M. G. Noaman, Ibrahim Abdulrab Ahmed, Jamil A. M. Saif
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 172 - Number 2
Year of Publication: 2017
Authors: Arafat A. Muharram, Khaled M. G. Noaman, Ibrahim Abdulrab Ahmed, Jamil A. M. Saif
10.5120/ijca2017915077

Arafat A. Muharram, Khaled M. G. Noaman, Ibrahim Abdulrab Ahmed, Jamil A. M. Saif . Optical Character Recognition based on Genetic Algorithms and Machine Learning. International Journal of Computer Applications. 172, 2 ( Aug 2017), 33-36. DOI=10.5120/ijca2017915077

@article{ 10.5120/ijca2017915077,
author = { Arafat A. Muharram, Khaled M. G. Noaman, Ibrahim Abdulrab Ahmed, Jamil A. M. Saif },
title = { Optical Character Recognition based on Genetic Algorithms and Machine Learning },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2017 },
volume = { 172 },
number = { 2 },
month = { Aug },
year = { 2017 },
issn = { 0975-8887 },
pages = { 33-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume172/number2/28225-2017915077/ },
doi = { 10.5120/ijca2017915077 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:19:16.615508+05:30
%A Arafat A. Muharram
%A Khaled M. G. Noaman
%A Ibrahim Abdulrab Ahmed
%A Jamil A. M. Saif
%T Optical Character Recognition based on Genetic Algorithms and Machine Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 172
%N 2
%P 33-36
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Pattern recognition is known to be one of the earliest applications of image processing. Genetic algorithm and Machine Learning have been used in this study to recognize English alphabets which are represented as matrix one and two dimensions. Genetic algorithm and machine learning were used in this paper to compare their efficiency and accuracy regarding concrete conditions, testing and evaluation results, it has ben got 95% for Genetic Algorithms and 94% for Machine Learning.

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Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithms character recognition machine learning.