Comparison Performance Evaluation of Modified Genetic Algorithm and Modified Counter Propagation Network for Online Character recognition.
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Date
2018-10-07
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Publisher
Transactions on Machine Learning and Artificial Intelligence.
Abstract
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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Citation
Adigun O. J., Fenwa, O. D. and Babatunde, R. S. (2018)