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Real-Time Arabic Sign Language Avatar by Naglaa Soliman is a document available to read on EtoBox.
This paper presents a real-time Arabic avatar system designed to assist deaf-mute individuals in communicating by translating text or spoken input into Arabic Sign Language (ArSL) gestures using deep learning techniques. The system employs the YOLOv8 model for gesture recognition, achieving a recognition accuracy of 99.4% on the AASL dataset, and aims to enhance communication within Arabic-speaking communities. The research addresses challenges in sign language interpretation and proposes a framework that c
- Author
- Naglaa Soliman
- Language
- EN