ASL Fingerspelling Recognition

Abstract

A method is proposed and software is developed for automatic recognition of gestures used in ASL fingerspelling. Static gestures are captured using the new generation 3D sensor Asus Xtion Pro Live. Gesture recognition is achieved by extracting and further comparing the normalized geometric skeletons of the hand. Hand skeletons are compared using Dynamic Time Warping algorithm, which has polynomial complexity.