Comparison Performance Evaluation of Modified Genetic Algorithm and Modified Counter Propagation Network for Online Character Recognition

  • Adigun Oyeranmi. J Department of Computer Technology, School of Technology, Yaba College of Technology, Yaba, Lagos Nigeria
  • Fenwa Olusayo D Department of Computer Science and Engineering, Faculty of Engineering and Technology, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.
  • Babatunde. Ronke. S Department of Computer Science, College of Information and Communication Technology, Kwara State University, Malete, Nigeria.
Keywords: Character recognition, normalization, Modified genetic Algorithm(MGA), Modified counter propagation network (MCPN), Generation gap


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.


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How to Cite
J, A. O., D, F. O., & S, B. R. (2018). Comparison Performance Evaluation of Modified Genetic Algorithm and Modified Counter Propagation Network for Online Character Recognition. Transactions on Machine Learning and Artificial Intelligence, 6(1), 81.