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Recognition of Odia Handwritten Digits using Gradient based Feature Extraction Method and Clonal Selection Algorithm

Recognition of Odia Handwritten Digits using Gradient based Feature Extraction Method and Clonal Selection Algorithm

Puspalata Pujari, Babita Majhi
Copyright: © 2019 |Volume: 6 |Issue: 2 |Pages: 15
ISSN: 2334-4598|EISSN: 2334-4601|EISBN13: 9781522568452|DOI: 10.4018/IJRSDA.2019040102
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MLA

Pujari, Puspalata, and Babita Majhi. "Recognition of Odia Handwritten Digits using Gradient based Feature Extraction Method and Clonal Selection Algorithm." IJRSDA vol.6, no.2 2019: pp.19-33. http://doi.org/10.4018/IJRSDA.2019040102

APA

Pujari, P. & Majhi, B. (2019). Recognition of Odia Handwritten Digits using Gradient based Feature Extraction Method and Clonal Selection Algorithm. International Journal of Rough Sets and Data Analysis (IJRSDA), 6(2), 19-33. http://doi.org/10.4018/IJRSDA.2019040102

Chicago

Pujari, Puspalata, and Babita Majhi. "Recognition of Odia Handwritten Digits using Gradient based Feature Extraction Method and Clonal Selection Algorithm," International Journal of Rough Sets and Data Analysis (IJRSDA) 6, no.2: 19-33. http://doi.org/10.4018/IJRSDA.2019040102

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Abstract

This article aims to recognize Odia handwritten digits using gradient-based feature extraction techniques and Clonal Selection Algorithm-based (CSA) multilayer artificial neural network (MANN) classifier. For the extraction of features which contribute the most towards recognition from images, are extracted using gradient-based feature extraction techniques. Principal component analysis (PCA) is used for dimensionality reduction of extracted features. A MANN is used as a classifier for classification purposes. The weights of the MANN are adjusted using the CSA to get optimized set of weights. The proposed model is applied on Odia handwritten digits taken from the Indian Statistical Institution (ISI), Calcutta, which consists of four thousand samples. The results obtained from the experiment are compared with a genetic-based multi-layer artificial neural network (GA-MANN) model. The recognition accuracy of the CSA-MANN model is found to be 90.75%.