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