Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper


January Edition 2023

International Journal of Applied Information Systems solicits high quality original research papers for the January 2023 Edition of the journal. The last date of research paper submission is December 15, 2022.

Development of a Genetic based Neural Network System for Online Character Recognition

J. O. Adigun, E. O. Omidiora, S. O. Olabiyisi, O. D. Fenwa, O. Oladipo, M. M. Rufai Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication: 2015
© 2015 by IJAIS Journal
Download full text
  1. J O Adigun, E O Omidiora, S O Olabiyisi, O D Fenwa, O Oladipo and M M Rufai. Article: Development of a Genetic based Neural Network System for Online Character Recognition. International Journal of Applied Information Systems 9(3):1-8, June 2015. BibTeX

    	author = "J. O. Adigun and E. O. Omidiora and S. O. Olabiyisi and O. D. Fenwa and O. Oladipo and M. M. Rufai",
    	title = "Article: Development of a Genetic based Neural Network System for Online Character Recognition",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 9,
    	number = 3,
    	pages = "1-8",
    	month = "June",
    	note = "Published by Foundation of Computer Science, New York, USA"


Character Recognition has been one of the most intensive research during the last few decades because of its potential applications. However, most existing classifiers used in recognizing online handwritten characters suffer from poor feature selection and slow convergence which affect training time and recognition accuracy. Hence, this paper focused on integrating an optimization (genetic algorithm) into modified backpropagation neural network to enhance the performance of character recognition. This paper proposed a methodology that is based on extraction of features using stroke number, invariant moments, projection and zoning. Genetic algorithm was use as feature selection to optimize the subset of the character for classification. A Modified Genetic Algorithm (MGA) was modified to reduce character recognition errors using fitness function and genetic operators. However, an integration of optimization algorithm (modified genetic algorithm) into an existing modified backpropagation (MOBP) learning algorithm was employed as classifier. For further enhancement of classifier, three classifiers (C1, C2 and C3) were formulated from MGA-MOBP model and evaluated using training time and correct recognition accuracy. C3 performed better than C1 and C2 in terms of convergence rate, correct recognition accuracy and feature selection (its ability to remove irrelevant features of character images). The results of the developed system achieved a false recognition of 0.56% and 99.44% overall recognition accuracy compared with existing models.


  1. Agnihotri, V. P. (2012): “Offline Handwritten Devanagari Script Recognition”, Information Technology and Computer Science, (8): 37-42. (
  2. Ayyaz, M. N., Javed, I. and Mahmood, W. (2012): “Handwritten Character Recognition Using Multiclass SVM Classification with Hybrid Feature Extraction”, Pak. J. Engg. & Appl. Sci. 10: 57-67.k
  3. Fenwa, O D, Omidiora, E, O and Fakolujo, O. A. (2012): “Development of a Feature Extraction Technique for Online Character Recognition System”, Innovative System Design and Engineering ISSN 2222-2871(Online) 3(3):10-23
  4. Fenwa, O. D., Omidiora, E, O., Fakolujo, O. A., Ajala, F. A., Oke, A. O. (2012): “A Modified Optical Backpropagation Neural Network Model in Online Handwritten Character Recognition System”, International Journal of Computer Application 2 4(2): 190-201.
  5. Haykin, S. (2003): “Neural Networks: A comprehensive Foundation”, PHI New-Delhi, India
  6. Ibrahim A. Adeyanju, Olusayo D. Fenwa, Elijah O. Omidiora(2014): "Effect of Non-image Features on recognition of handwritten alpha-numeric characters" International Journal of Computers and Technology(IJCT).13(11):5155-5161.
  7. Jumanal Shilpa and Holi, Ganga (2013): “On-line Handwritten English Character Recognition Using Genetic Algorithm” International Journal of Computer Trends and Technology (IJCTT). 4(6): 1885-1890.
  8. Muhammad, F. Z., Dzulkifli, M. and Razib, M. O. (2006): “Writer Independent Online Handwritten Character Recognition Using a simple Approach”, Information Technology Journal 5(3): 476-484.
  9. Omidiora E. O., Oyediran, G. O., Olabiyisi, S. O. and Arulogun, O. T. (2008): ”Classification of Soils of Central Western Nigeria using Neural Network Rule Extraction and Decision Table, Agricultural Journal 3(4):305-312
  10. Omidiora, E. O., Oladipo, O., Oyeleye ,C. A and Ismaila W. O. (2013) A Study of Genetic Principal Component Analysis (GPCA)in feature extraction and recognition of face images, Journal of Computer Science And Engineering Vol. 19, issue 1.
  11. Omidiora, E. O., Adeyanju, I. A. Oladipo., Fenwa, O. D. (2014) ”Comparison of Machine Learning Classifiers For Recognition of Face Images”, Journal Of Computer Science And Engineering 19(1):1-5
  12. Padhi, D (2012):"Novel Hybrid Approach for Odia handwritten Character Recognition System", International Jornal of Advanced Research in Computer Science and Software Engineering, 2(5):150-157
  13. Ranpreet, K. and Singh, B. (2011): “A Hybrid Neural Approach for Character Recognition System”, (IJCSIT) International Journal of Computer Science and Information Technologies, 2 (2):721-726.
  14. Razzak, M. I., Hussain, S.A. and Mirza,A.A(2012):“Bio-Inspired Multilayered and Multilanaguage Arabic Script Character Recognition System”, International Journal of Innovative Computing, Information and Control . 6(4):2681-2691.
  15. Yeremia Hendy, Niko Adrianus Yuwono, Pius Raymond and Widodo Budiharto (2013): “Genetic Algorithm and Neural Network for Optical character recognition” Journal of computer science 9 (11): 1435-1442.
  16. Sutojo, T. E. Mulyanto and V. Suhartono, 2011. Keeedarsan buatan . ANDI, Yogyakarta


Artificial Neural Network, optical backpropagation, genetic algorithm, character recognition, feature extraction, feature selection, genetic operators.