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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 4/2017

09.04.2017

Identification of ISL Alphabets Using Discrete Orthogonal Moments

verfasst von: Bineet Kaur, Garima Joshi, Renu Vig

Erschienen in: Wireless Personal Communications | Ausgabe 4/2017

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Abstract

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.
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Metadaten
Titel
Identification of ISL Alphabets Using Discrete Orthogonal Moments
verfasst von
Bineet Kaur
Garima Joshi
Renu Vig
Publikationsdatum
09.04.2017
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2017
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-017-4126-2

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