NDLI: Vision Based Multi-feature Hand Gesture Recognition for Indian Sign Language Manual Signs

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Vision Based Multi-feature Hand Gesture Recognition for Indian Sign Language Manual Signs

Content Provider	Semantic Scholar
Author	Kharate, Gajanan K. Ghotkar, Archana S.
Copyright Year	2016
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.
Starting Page	124
Ending Page	147
Page Count	24
File Format	PDF HTM / HTML
DOI	10.21307/ijssis-2017-863
Volume Number	9
Alternate Webpage(s)	https://www.exeley.com/exeley/journals/in_jour_smart_sensing_and_intelligent_systems/9/1/pdf/10.21307_ijssis-2017-863.pdf
Alternate Webpage(s)	https://doi.org/10.21307/ijssis-2017-863
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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