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An Approach to Recognize Handwritten Hindi Characters Using Substantial Zernike Moments With Genetic Algorithm

An Approach to Recognize Handwritten Hindi Characters Using Substantial Zernike Moments With Genetic Algorithm

Ajay Indian, Karamjit Bhatia
Copyright: © 2021 |Volume: 11 |Issue: 2 |Pages: 16
ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781799862031|DOI: 10.4018/IJCVIP.2021040105
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MLA

Indian, Ajay, and Karamjit Bhatia. "An Approach to Recognize Handwritten Hindi Characters Using Substantial Zernike Moments With Genetic Algorithm." IJCVIP vol.11, no.2 2021: pp.66-81. http://doi.org/10.4018/IJCVIP.2021040105

APA

Indian, A. & Bhatia, K. (2021). An Approach to Recognize Handwritten Hindi Characters Using Substantial Zernike Moments With Genetic Algorithm. International Journal of Computer Vision and Image Processing (IJCVIP), 11(2), 66-81. http://doi.org/10.4018/IJCVIP.2021040105

Chicago

Indian, Ajay, and Karamjit Bhatia. "An Approach to Recognize Handwritten Hindi Characters Using Substantial Zernike Moments With Genetic Algorithm," International Journal of Computer Vision and Image Processing (IJCVIP) 11, no.2: 66-81. http://doi.org/10.4018/IJCVIP.2021040105

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Abstract

A technique to recognize off-line handwritten Hindi character is suggested by employing Zernike complex moments like a tool to describe the characteristics of a character. Further, an algorithm for selecting the features is employed to identify the substantial image moments from the extracted moments, as the extracted moments may have some insignificant ones. Insignificant moments can increase the computational time and can also degrade the classification accuracy. Thus, the objectives of the study are twofold: (1) to find the important Zernike moments by employing the Genetic algorithm (GA) and (2) the classification of each character is performed using neural network. This way, the performance of the proposed technique is evaluated on two parameters (i.e., speed and recognition accuracy). Further, the efficacy of GA for selecting the moment features is assessed, and the efficacy of selected Zernike complex moments using GA is analyzed for handwritten Hindi characters. Here, the authors used a resilient backpropagation learning algorithm (RPROP) as a classification model.

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