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<p>Indian sign language (ISL) is the main communication medium among deaf Indians. An ISL vocabulary show that the hand plays a significant role in ISL. ISL includes static and dynamic hand gesture recognition. The main aim of this paper is to present multi-feature static hand gesture recognition for alphabets and numbers. Here, comparative analysis of various feature descriptors such as chain code, shape matrix, Fourier descriptor, 7 Hu moments, and boundary moments is done. Multi-feature fusion descriptor is designed using contour (Boundary moments, Fourier descriptor) and region based (7Hu moments) descriptors. Analysis of this new multi-feature descriptor is done in comparison with other individual descriptors and it showed noteworthy results over other descriptors. Three classification methods such as, Nearest Mean Classifier (NMC), k-Nearest Neighborhood (k-NN) and Naive Bayes classifier are used for classification and comparison. New Multi-feature fusion descriptor shows high recognition rate of 99.61% among all with k-NN. Real time recognition for number signs 0-9, of fusion descriptor with NMC gave 100% accuracy</p>
</abstract>

Indian sign language (ISL) is the main communication medium among deaf Indians. An ISL vocabulary show that the hand plays a significant role in ISL. ISL includes static and dynamic hand gesture recognition. The main aim of this paper is to present multi-feature static hand gesture recognition for alphabets and numbers. Here, comparative analysis of various feature descriptors such as chain code, shape matrix, Fourier descriptor, 7 Hu moments, and boundary moments is done. Multi-feature fusion descriptor is designed using contour (Boundary moments, Fourier descriptor) and region based (7Hu moments) descriptors. Analysis of this new multi-feature descriptor is done in comparison with other individual descriptors and it showed noteworthy results over other descriptors. Three classification methods such as, Nearest Mean Classifier (NMC), k-Nearest Neighborhood (k-NN) and Naive Bayes classifier are used for classification and comparison. New Multi-feature fusion descriptor shows high recognition rate of 99.61% among all with k-NN. Real time recognition for number signs 0-9, of fusion descriptor with NMC gave 100% accuracy

Vision Based Multi-Feature Hand Gesture Recognition For Indian Sign Language Manual Signs

Department of Electronics and Telecommunication Engineering, Savitribai Phule Pune University

Department of Computer Engineering, (Research Scholar in I2IT, Hinjawadi, Pune) Savitribai Phule Pune University

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Indian sign language (ISL) is the main communication medium among deaf Indians. An ISL vocabulary show that the hand plays a significant role in ISL. ISL...

Vision Based Multi-Feature Hand Gesture Recognition For Indian Sign Language Manual Signs

Publié en ligne: 01 mars 2016

Pages: 124 - 147

Reçu: 30 janv. 2016

Accepté: 04 janv. 2016

DOI: https://doi.org/10.21307/ijssis-2017-863

Mots clés
Indian sign language interpretation, k-nearest neighborhood classifier, nearest mean Classifier, Boundary moments, Fourier descriptor, 7 Hu moments, Chain code, Shape matrix, Naïve Bayes

© 2016 Gajanan K. Kharate et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Vision Based Multi-Feature Hand Gesture Recognition For Indian Sign Language Manual Signs

Publié en ligne: 01 mars 2016

Pages: 124 - 147

Reçu: 30 janv. 2016

Accepté: 04 janv. 2016

DOI: https://doi.org/10.21307/ijssis-2017-863

Mots clésIndian sign language interpretation, k-nearest neighborhood classifier, nearest mean Classifier, Boundary moments, Fourier descriptor, 7 Hu moments, Chain code, Shape matrix, Naïve Bayes

© 2016 Gajanan K. Kharate et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Indian sign language interpretation, k-nearest neighborhood classifier, nearest mean Classifier, Boundary moments, Fourier descriptor, 7 Hu moments, Chain code, Shape matrix, Naïve Bayes