American Sign Language (ASL) recognition systems often struggle with accuracy due to similar gestures, poor image quality and inconsistent lighting. To address this, researchers developed a system that translates gestures into text with 98.2% accuracy, operating in real time under varying conditions. Using a standard webcam and advanced tracking, it offers a scalable solution for real-world use, with MediaPipe tracking 21 keypoints on each hand and YOLOv11 classifying ASL letters precisely.
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