Comparison Performance Evaluation of Modified Genetic Algorithm and Modified Counter Propagation Network for Online Character Recognition
This paper carries out performance evaluation of a Modified Genetic Algorithm (MGA) and Modified Counter Propagation Network (MCPN) Network for online character recognition. Two techniques were used to train the feature vectors using supervised and unsupervised methods. First, the modified genetic algorithm used feature selection to filter irrelevant features vectors and improve character recognition because of its stochastic nature. Second, MCPN has its capability to extract statistical properties of the input data. MGA and MCPN techniques were used as classifiers for online character recognition then evaluated using 6200-character images for training and testing with best selected similarity threshold value. The experimental results of evaluation showed that, at 5 x 7 pixels, MGA had 97.89% recognition accuracy with training time of 61.20ms while MCPN gave 97.44% recognition accuracy in a time of 62.46ms achieved. At 2480, MGA had 96.67% with a training time of 4.53ms, whereas MCPN had 96.33% accuracy with a time of 4.98ms achieved. Furthermore, at 1240 database sizes, MGA has 96.44 % recognition accuracy with 0.62ms training time whereas MCPN gave 96.11% accuracy with 0.75ms training time. The two techniques were evaluated using different performance metrics. The results suggested the superior nature of the MGA technique in terms of epoch, recognition accuracy, convergence time, training time and sensitivity.
(1) Abed Majida Ali, Ismail Ahmad Nasser and Hazi Zubadi Matiz., Pattern recognition Using Genetic Algorithm, International Journal of Computer and Electrical Engineering, 2013. 2(3): p. 583-588.
(2) Adigun J.O., Fenwa O.D., Omidiora E.O., Olabiyisi S.O. Optimized features for genetic based neural network model for online character recognition. British Journal of Mathematics & Computer Science, 2016, 14(6):1-13.
(3) Herekar Rachana R. , S. R. Dhotre, Handwritten Character Recognition Based on Zoning Using Euler Number for English Alphabets and Numerals. In IOSR e-ISSN: 2278-0661, 2014, 16(4), p.75-88.
(4) Adigun J.O., Omidiora E.O., Olabiyisi S.O., Fenwa O.D., Oladipo O, Rufai M.M. Development of a Genetic based neural network system for online character recognition. International Journal of Applied Information Systems (IJAIS), 2015, 9(3), p.1-8.
(5) Noaman, Khaled M.G, Saif, Jamil Abdulhameed M., Alqubati, Ibrahim A.A, Optical Character Recognition Based on Genetic Algorithms, Journal of Emerging Trends in Computing and Information Sciences, 2015, 6(4), p.204-208.
(6) Yeremia Hendy, Niko Adrianus Yuwono, Pius Raymond and Widodo Budiharto, Genetic Algorithm and Neural Network for Optical character recognition, Journal of computer science, 2013, 9 (11),1435-1442.
(7) Fenwa, O.D., Omidiora, E.O. and Fakolujo, O.A. Development of a Feature Extraction Technique for Online Character Recognition System, Journal of Innovative System Design and Engineering, International Institute of Science, Technology and Education, New York, USA, 2012, 3(3),
(8) Kumar D, Rai CS, Kumar S. An experimental comparison of unsupervised learning techniques for face recognition. International Journal of Computer and Information Science and Engineering. 2007,1(3):158-166.
(9) Inamdar Farhan, and Bagal, S. B. (2016): Comparative Study of Optical Character Recognition Techniques, International Journal of Innovative Research in Computer and Communication Engineering, 2016,4(11), p.19831-19837
(10) Oyeranmi Adigun, Elijah Omidiora and Mohammed Rufai, Modified Genetic Algorithm Parameters to Improve Online Character Recognition, British Journal of Applied Science & Technology, 2016, 18(5): 1-8.
(11) Olusayo D. Fenwa, Funmilola A. Ajala and Alice O. Oke, A PSO-Based Modified Counter Propagation Neural Network Model for Online Handwritten Character Recognition System, International Journal of Emerging Technology and Advanced Engineering, 2014, 4(6), p.768 -776
(12) Biswas, Mithun and Parekh, Ranjan, Character Recognition using Dynamic Windows, International Journal of Computer Applications, 2012, 41(15):47-52.
(13) Chaudhari Prasad P, Sarode KR. Offline handwritten character recognition by using grid approach. International Journal of Application or Innovation in Engineering & Management, 2014, 3(4), p.71-73.
(14) Kaur Tarandeep and Chabbra Amit, Genetic Algorithm Optimized Neural Network for Handwritten Character Recognition, International Journal of Computer Applications, 2015, 119(24), p.22-26