Sign Language Gesture Recognition Using YOLOv9 for Medical Attention of Hard of Hearing Population

For the hard-of-hearing population, communicating medical issues to doctors who do not understand sign language can be challenging. To address this problem, researchers have focused extensively on sign language gesture recognition. However, existing methods often struggle with conversion time and the accuracy of recognizing similar gestures. In this paper, we propose a sign language gesture recognition system utilizing the Yolov9 model. The system's performance was evaluated using a publicly available Kaggle dataset, achieving an impressive Mean Average Precision ([email protected]) of 99.5%. Experimental results show that the proposed method surpasses state-of-the-art techniques in both efficiency and accuracy.