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

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