  Sign Language helps people with Speaking and Hearing Disabilities communicate
with others efficiently. Sign Language identification is a challenging area in
the field of computer vision and recent developments have been able to achieve
near perfect results for the task, though some challenges are yet to be solved.
In this paper we propose a novel machine learning based pipeline for American
Sign Language identification using hand track points. We convert a hand gesture
into a series of hand track point coordinates that serve as an input to our
system. In order to make the solution more efficient, we experimented with 28
different combinations of pre-processing techniques, each run on three
different machine learning algorithms namely k-Nearest Neighbours, Random
Forests and a Neural Network. Their performance was contrasted to determine the
best pre-processing scheme and algorithm pair. Our system achieved an Accuracy
of 95.66% to identify American sign language gestures.
