Real-time American sign language recognition using desk and wearable computer based video
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.
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Hand gesture recognition can be very useful for interactions with things in AIoT systems, such as non-contact control of television and car infotainment system, and communication with the speech and hearing impaired.
“Hand gesture recognition can be very useful for interactions with things in AIoT systems, e.g., non-contact control of television and car infotainment system, and communication with the speech and hearing impaired [125].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [125]
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- Authors
- Starner, T., Weaver, J., & Pentland, A.
- Affiliations
- MIT Media Lab (US)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1998
- Volume
- 20
- Issue
- 12
- Pages
- 1371-1375
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2146221819
- Cited by
- 1,335
- References
- 0
- Citations by year
- 2026: 27 · 2025: 40 · 2024: 92 · 2023: 47 · 2022: 37 · 2021: 46 · 2020: 55 · 2019: 58 · 2018: 40 · 2017: 67 · 2016: 64 · 2015: 62 · 2014: 58 · 2013: 60 · 2012: 55
- FWCI
- 24.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- Funders
- MIT Media Lab
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- American Sign Language recognition, real-time sign language recognition, hidden Markov models, continuous sign language recognition, hand tracking, wearable cameras, single-camera video
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