Arabic sign language recognition by decisions fusion using Dempster-Shafer theory of evidence

Most sign language recognition systems that use gloves and hand trackers combine the data from both devices at the sensor level. In this paper we propose a new approach by combining information acquired from the gloves and the hand tracking systems at the decision level using the Dempster-Shafer theory of evidence. The results using the Dempster-Shafer on the recognition of 100 two-handed signs show enhanced performance compared to the individual systems and to classification based on combined features. A recognition accuracy of 84.7%, and 91.3% are achieved when attempting to recognize the signs from the hand tracker only, and the glove data, respectively. When the sensor data from the gloves and hand tracking systems are combined, a recognition accuracy of 96.2% was achieved while a recognition accuracy of 98.1% was achieved when the fusion is performed at the decision level using Dempster-Shafer theory of evidence.