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

  • Authors:
  • Thad Starner;Alex Pentland;Joshua Weaver

  • Affiliations:
  • Massachusetts Institute of Technology, Cambridge;Massachusetts Institute of Technology, Cambridge;Massachusetts Institute of Technology, Cambridge

  • Venue:
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Year:
  • 1998

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Abstract

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.