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Please use this identifier to cite or link to this item: http://idr.iitbbs.ac.in/jspui/handle/2008/808
Title: On extraction of features for handwritten Odia numeral recognition in transformed domain
Authors: Dash K.S.
Puhan N.B.
Panda G.
Keywords: handwritten character recognition
Odia
slantlet
stockwell
transformed domain feature
Issue Date: 2015
Citation: 5
Abstract: Recognition of handwritten scripts has always been a challenging task before the character recognition community. The difficulty lies in the fact that different individuals have different writing styles and hence there is a lot of intra-class pattern variation. Several feature extraction techniques based on statistical, structural properties have been reported in literature. We, in this paper, propose a number of image transformation based feature extraction techniques such as, Slantlet transform based, Stockwell transform based, and Gabor-wavelet based transformed domain features for offline Odia handwritten numeral recognition. The performances of the proposed methods are evaluated on ISI Kolkata Odia numeral database with a nearest neighbor classifier and the recognition accuracies are reported. � 2015 IEEE.
URI: http://dx.doi.org/10.1109/ICAPR.2015.7050694
http://10.10.32.48:8080/jspui/handle/2008/808
Appears in Collections:Research Publications

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