Real-time sign-language recognition
Computer-vision research into real-time, multi-user sign spelling, recognition, and translation.
Research summary
This work studies real-time sign spelling and translation in multi-user settings, combining object detection, skeletal cues, and real-time inference.
Methods
Visual models recognize gestures and motion sequences while evaluating latency and stability in multi-person scenes.
Outcomes
Related outcomes include a WASN 2025 honorable mention student paper and collaboration on an IEEE ICCBE 2025 Best Paper.