Trajectory based Arabic sign language recognition

Deaf and hearing impaired people use their hand as a tongue to convey their thoughts by performing descriptive gestures that form the sign language. A sign language recognition system is a system that translates these gestures into a form of spoken language. Such systems are faced by several challenges, like the high similarities of the different signs, difficulty in determining the start and end of signs, lack of comprehensive and bench marking databases. This paper proposes a system for recognition of Arabic sign language using the 3D trajectory of hands. The proposed system models the trajectory as a polygon and finds features that describes this polygon and feed them to a classifier to recognize the signed word. The system is tested on a database of 100 words collected using Kinect. The work is compared with other published works using publicly available dataset which reflects the superiority of the proposed technique. The system is tested for both signer-dependent and signer-independent recognition.