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Sign language is a gesture-based symbolic communication medium among speechand hearing impaired people. It also serves as a communication bridge betweennon-impaired and impaired populations. Unfortunately, in most situations, anon-impaired person is not well conversant in such symbolic languagesrestricting the natural information flow between these two categories.Therefore, an automated translation mechanism that seamlessly translates signlanguage into natural language can be highly advantageous. In this paper, weattempt to perform recognition of 30 basic Indian sign gestures. Gestures arerepresented as temporal sequences of 3D maps (RGB + depth), each consisting of3D coordinates of 20 body joints captured by the Kinect sensor. A recurrentneural network (RNN) is employed as the classifier. To improve the classifier'sperformance, we use geometric transformation for the alignment correction ofdepth frames. In our experiments, the model achieves 84.81% accuracy.

Position and Rotation Invariant Sign Language Recognition from 3D Kinect Data with Recurrent Neural Networks