Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video

—We present two real-time hidden Markov model-based systems for recognizing sentence-level continuous American Sign Language (ASL) using a single camera to track the user’s unadorned hands. The first system observes the user from a desk mounted camera and achieves 92 percent word accuracy. The second system mounts the camera in a cap worn by the user and achieves 98 percent accuracy (97 percent with an unrestricted grammar). Both experiments use a 40-word lexicon.

• Type: Journal
• Year: 1998
• Where: PAMI
• Authors: Thad Starner, Joshua Weaver, Alex Pentland
• Keywords: Continuous American Sign, Markov Model-based Systems, PAMI 1998, Percent Word Accuracy