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Identification of ISL Alphabets Using Discrete Orthogonal Moments

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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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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, UIET, Panjab University, Chandigarh, India

    Bineet Kaur, Garima Joshi & Renu Vig

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  1. Bineet Kaur
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  2. Garima Joshi
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  3. Renu Vig
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Correspondence to Bineet Kaur.

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Kaur, B., Joshi, G. & Vig, R. Identification of ISL Alphabets Using Discrete Orthogonal Moments. Wireless Pers Commun 95, 4823–4845 (2017). https://doi.org/10.1007/s11277-017-4126-2

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  • DOI: https://doi.org/10.1007/s11277-017-4126-2

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