The week's pick
Reseach Article
A Modified Genetic based Neural Network Model for Online Character Recognition
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 9 - Number 5 |
| Year of Publication: 2015 |
| Authors: J. O. Adigun, E. O. Omidiora, S. O. Olabiyisi, O. D. Fenwa |
10.5120/ijais2015451412
|
J. O. Adigun, E. O. Omidiora, S. O. Olabiyisi, O. D. Fenwa . A Modified Genetic based Neural Network Model for Online Character Recognition. International Journal of Applied Information Systems. 9, 5 ( August 2015), 18-23. DOI=10.5120/ijais2015451412
Abstract
Character Recognition has become an intensive research areas during the last few decades because of its potential applications. However, most existing classifiers used in recognizing handwritten online characters suffer from poor feature selection and slow convergence which affect training time and recognition accuracy. This paper proposed a methodology that is based on extraction of structural features (invariant moment, stroke number and projection) and a statistical feature (zoning) from the characters. A genetic algorithm was modified through its fitness function and genetic operators to minimize the character recognition errors. The Modified Genetic Algorithm (MGA) was used to select optimized feature subset of the character to reduce the number of insignificant and redundant features. A genetic based neural network model was developed by integrating the MGA into an existing Modified Optical Backpropagation (MOBP) learning algorithm to train the network. Three classifiers (C1, C2 and C3) were then formulated from MGA-MOBP such that C1 classified without using MGA at classification level, C2 classified using MGA at classification level while C3 employed MGA at feature selection level and classified at classification level The developed C3 achieves a better performance of recognition accuracy and recognition time.
References
- Agnihotri, V. P. (2012): “Offline Handwritten Devanagari Script Recognition”, Information Technology and Computer Science, (8): 37-42.
- Ashutosh, A, R. Rajneesh and RenuDhir (2012): “Handwritten Devanagari Character Recognition Using Gradient Features,”International Journal of Advanced Research in Computer Science and Software Engineering, (2)5.
- 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.
- 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
- 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.
- Haykin, S. (2003): “Neural Networks: A comprehensive Foundation”, PHI New-Delhi, India
- 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.
- 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.
- 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.
- 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
- 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 ….
- 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
- 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.
- Pradeep, J., Srinivasan, E. and Himavathi, S. (2011): “Diagonal Based Feature Extraction for handwritten Alphabets Recognition system using Neural Network”, International Journal of Computer Science & Information Technology (IJCSIT), 3(1): 27-38.
- 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.
- 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.
- Yeremia Hendy, Niko Adrianus Yuwono, Pius Raymond and Widodo Budiharto (2013): “Genetic Algorithm and Neural Network for Optical character recogn ition” Journal of computer science 9 (11): 1435-1442.
Index Terms
Keywords