A Multi-Class Pattern Recognition System for Practical Finger Spelling Translation

This paper presents a portable system and method for recognizing the 26 hand shapes of the American Sign Language alphabet, using a novel glove-like device. Two additional signs, 'space', and 'enter ' are added to the alphabet to allow the user to form words or phrases and send them to a speech synthesizer. Since the hand shape for a letter varies from one signer to another, this is a 28-class pattern recognition system. A three-level hierarchical classifier divides the problem into "dispatchers " and "recognizers. " After reducing pattern dimension from ten to three, the projection of class distributions onto horizontal planes makes it possible to apply simple linear discrimination in 2D, and Bayes ' Rule in those cases where classes had features with overlapped distributions. Twenty-one out of 26 letters were recognized with 100 % accuracy; the worst case, letter U, achieved 78%