Implementation of dynamic time warping for gesture recognition in sign language using high performance computing

ASL (American Sign Language) is the primary language of many who are deaf. ASL is a complex language that employs signs made by moving the hands combined with facial expressions and postures of the body expression to convey linguistic information. Designed system for sign language recognizer works for gestures in ASL. Kinect is used as image capture device and fits the low-cost requirement as well. Human skeleton data of the joints of a user captured by the Kinect are analyzed. Video is runtime processed for signs. If gesture is predefined in the library, it is transcribed to word or phrase, and output is presented as voice and text. The implemented system works with excellent accuracy. After parallel implementation for system it achieves 95.6% in accuracy. This recognizer can be used as tutor for those who want to learn Sign language as well as translator for Deaf people so that they can communicate efficiently with everyone.