Identification of ISL Alphabets Using Discrete Orthogonal Moments

Identification of ISL Alphabets Using Discrete Orthogonal Moments In this paper, discrete orthogonal moment-based shape features up to 5th order are proposed for Indian sign language (ISL) recognition system. The shape recognition capability of discrete orthogonal moment-based local features is verified on two databases. These include the standard Jochen-Triesch’s database and 26 ISL alphabets. The ISL alphabets are collected on both uniform and complex backgrounds, with variations in position, scale and rotation. The feature-set is increased for 26 ISL alphabets by varying Region of Interest (ROI) and extracting features from each ROI. A minimum possible feature-set with least redundancy is selected that gives the best recognition accuracy. The effect of order and feature dimensionality for different classifiers is studied. Results show that both Dual-Hahn and Krawtchouk moments are found to exhibit user, scale, rotation and translation invariance. Moreover, they have shape identification capability, thus achieving good recognition accuracy. Wireless Personal Communications Springer Journals

Identification of ISL Alphabets Using Discrete Orthogonal Moments

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Springer US
Copyright © 2017 by Springer Science+Business Media New York
Engineering; Communications Engineering, Networks; Signal,Image and Speech Processing; Computer Communication Networks
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