Researchers at Florida Atlantic University have developed a revolutionary ASL interpretation system leveraging YOLOv11 and MediaPipe for real-time gesture recognition. This innovative approach seeks to address the long-standing communication barriers faced by deaf and hard-of-hearing individuals, who often find traditional sign language solutions inadequate due to cost and availability. The system intelligently, and accurately, recognizes hand gestures by utilizing advanced deep learning techniques and key hand point tracking to translate ASL into text, promoting effective interaction and accessibility in various environments.
The new ASL interpretation system developed by researchers tackles communication barriers by combining YOLOv11 and MediaPipe for real-time accurate ASL gesture recognition.
By addressing issues like dataset quality and real-time performance, this innovative system aims to enhance accessibility for millions of deaf and hard-of-hearing individuals.
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