Sign language recognition using dynamic time warping and hand shape distance based on histogram of oriented gradient features

Recognizing sign language is a very challenging task in computer vision. One of the more popular approaches, Dynamic Time Warping (DTW), utilizes hand trajectory information to compare a query sign with those in a database of examples. In this work, we conducted an American Sign Language (ASL) recognition experiment on Kinect sign data using DTW for sign trajectory similarity and Histogram of Oriented Gradient (HoG) [5] for hand shape representation. Our results show an improvement over the original work of [14], achieving an 82% accuracy in ranking signs in the 10 matches. In addition to our method that improves sign recognition accuracy, we propose a simple RGB-D alignment tool that can help roughly approximate alignment parameters between the color (RGB) and depth frames.