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Reseach Article

Genetic Algorithm Optimized Neural Network for Handwritten Character Recognition

by Tarandeep Kaur, Amit Chabbra
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 119 - Number 24
Year of Publication: 2015
Authors: Tarandeep Kaur, Amit Chabbra
10.5120/21385-4391

Tarandeep Kaur, Amit Chabbra . Genetic Algorithm Optimized Neural Network for Handwritten Character Recognition. International Journal of Computer Applications. 119, 24 ( June 2015), 22-26. DOI=10.5120/21385-4391

@article{ 10.5120/21385-4391,
author = { Tarandeep Kaur, Amit Chabbra },
title = { Genetic Algorithm Optimized Neural Network for Handwritten Character Recognition },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 119 },
number = { 24 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 22-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume119/number24/21385-4391/ },
doi = { 10.5120/21385-4391 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:04:54.971590+05:30
%A Tarandeep Kaur
%A Amit Chabbra
%T Genetic Algorithm Optimized Neural Network for Handwritten Character Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 119
%N 24
%P 22-26
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Handwritten Character Recognition is well known problem which has many real world applications. Many solutions have already been proposed using various techniques (neural networks, fuzzy rules etc. ) over a period of time, but no one is able to achieve 100 percent accuracy rate. Involvement of various organizations for research on handwriting recognition has been significantly exaggerated over last few decades. Solution is required which can provide higher accuracy rate in lesser amount of computation time. This paper covers introduction to problem and various terms used, proposed solution based upon Neural Networks whose weights have been optimized using Genetic Algorithm (GA) with newly designed fitness function and performance comparison of proposed design with existing techniques various constraints.

References
  1. David. E. Goldberg, "Genetic algorithms in Search, Optimization & Machine learning", Addison Wesley, Publishing Co. Inc. ,Boston, MA, pp- 1-25, 1990.
  2. Melanie Mitchell, "An Introduction to Genetic algorithms", The MIT press, Cambridge, Massachusetts, England, 5th printing, pp 2-20, 1999.
  3. Pratibha Bajpai et al. , "Genetic Algorithm- an Approach to Solve Global Optimization Problems", Indian Journal of Computer Science and Engineering, 2010.
  4. K. Fukushima, "Neocognitron for handwritten digit recognition" Neurocomputing, Vol. 51 ,pp. 161– 180 ,2003.
  5. Velappa Ganapathy, and Kok Leong Liew, "Handwritten Character Recognition Using Multiscale Neural Network Training Technique", World Academy of Science, Engineering and Technology 15, 2008.
  6. Neera Saxena1, Qasima Abbas Kazmi, Chandra Pal and, Prof. O. P. Vyas, "Employing Neocognitron Neural Network Base Ensemble Classifiers To Enhance Efficiency Of Classification In Handwritten Digit Datasets", D. C. Wyld, et al. (Eds): CCSEA 2011, CS & IT 02, pp. 408–416, 2011. © CS & IT-CSCP 2011.
  7. Mamedov, Fakhraddin and Jamal Fathi Abu Hasna, "Character Recognition Using Neural Networks", Near East University, North Cyprus, Turkey via Mersin-10, KKTC.
  8. Faisal Mohammad and Jyoti Anarase, "Optical Character Recognition Implementation Using Pattern Matching", International Journal of Computer Science and InformationTechnologies, Vol. 5 (2) , 2014.
Index Terms

Computer Science
Information Sciences

Keywords

Optimization Neural Network