Breaking barriers: Study uses AI to interpret American Sign Language in real-time
A study is the first-of-its-kind to recognize American Sign Language (ASL) alphabet gestures using computer vision. Researchers developed a custom dataset of 29,820 static images of ASL hand gestures. Each image was annotated with 21 key landmarks on the hand, providing detailed spatial information about its structure and position. Combining MediaPipe and YOLOv8, a deep learning method they trained, with fine-tuning hyperparameters for the best accuracy, represents a groundbreaking and innovative approach that hasn’t been explored in previous research.
A Cornell-led research team has developed an artificial intelligence-powered ring equipped with micro-sonar technology that can continuously—and in real time—track fingerspelling in American Sign Language (ASL).
Many computer systems that people interact with on a daily basis require knowledge about certain aspects of the world, or models, to work. These systems have to be trained, often needing to learn how to recognize objects from video or image data. This data frequently contains superfluous content that reduces…