Abstract
Handwritten character recognition is one of the emerging areas in pattern recognition and deep learning application. The character recognition reads transcript in natural images which is a vital step with diversity of computer vision jobs and has significant achievement in several profitable applications. This paper deals with the hybrid optimization approach with the performance analysis based on the automatic classification of the handwritten English characters in an efficient manner. The proposed approach deals with the feature extraction and instance selection using independent component analysis and hybrid PSO and firefly optimization for the effectual feature selection approach and then the automatic classification is done using supervised learning process named as backpropagation neural network. The results show that the proposed approach is able to achieve high precision rate and accuracy to reduce the error rate probabilities and increase the sensitivity and specificity for the proposed system to achieve high classification rates.
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Acknowledgements
The authors would like to express sincere gratitude to Dr. Ulhas B. Shinde, Principal, CSMSS, Chh. Shahu College of Engineering, Aurangabad, for his support and encouragement to publish this article. They would also like to thank Mr. Devendra L. Bhuyar, Mr. Amit M. Rawate, Mr. Mahendra G. Nakrani, and Mr. Yogesh H. Bhosale for their continuous help in this work.
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Zanwar, S.R., Shinde, U.B., Narote, A.S., Narote, S.P. (2020). Handwritten English Character Recognition Using Swarm Intelligence and Neural Network. In: Thampi, S., et al. Intelligent Systems, Technologies and Applications. Advances in Intelligent Systems and Computing, vol 1148. Springer, Singapore. https://doi.org/10.1007/978-981-15-3914-5_8
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DOI: https://doi.org/10.1007/978-981-15-3914-5_8
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