A Combinational Approach of Feature Extraction for Offline Handwritten Hindi Numeral Recognition
|Ajay Indian1 , Karamjit Bhatia2|
1 Department of Computer Science, Gurukula Kangri Vishvidyalaya, Haridwar and Invertis University, Bareilly, India.
2 Department of Computer Science, Gurukula Kangri Vishvidyalaya, Haridwar, India.
Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-5 , Page no. 1-8, May-2018
Online published on May 31, 2018
Copyright © Ajay Indian, Karamjit Bhatia . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Ajay Indian, Karamjit Bhatia, “A Combinational Approach of Feature Extraction for Offline Handwritten Hindi Numeral Recognition”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1-8, 2018.
MLA Style Citation: Ajay Indian, Karamjit Bhatia "A Combinational Approach of Feature Extraction for Offline Handwritten Hindi Numeral Recognition." International Journal of Computer Sciences and Engineering 6.5 (2018): 1-8.
APA Style Citation: Ajay Indian, Karamjit Bhatia, (2018). A Combinational Approach of Feature Extraction for Offline Handwritten Hindi Numeral Recognition. International Journal of Computer Sciences and Engineering, 6(5), 1-8.
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|Offline Handwritten Character Recognition is a very challenging field to work upon, as the handwriting of an individual differs very much from another individual, even the handwriting of an individual may differ on different times. Studies have shown that recognition efficiency of characters depends on the ways the features are extracted and formulated as the feature vector. A lot of techniques have been proposed by the various research scholars for feature extraction. In this paper, a combinational approach of feature extraction is proposed as combinational feature vectors (Gradient features, Zernike complex moment features, and Wave based features) may contribute to improved recognition rate. For training and testing purpose, samples of Hindi numerals from 0 to 9 are taken. A feature vector of directional gradient histogram (DGH), a feature vector of Zernike complex moments (ZCM) and a feature vector of Wave features (WF) are feed to the Back-propagation based Neural Network classifiers for training and recognition rate of approx. 79.7%, 92.7% and 73% are attained respectively. By combining the feature vectors DGH, CZM, and WF, a higher recognition rate of 96.4% is obtained for isolated Hindi Numerals.|
|Key-Words / Index Term :|
|Character Recognition, Gradient features, Zernike Moments, Wave features, Backpropagation Neural Network|
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