The Use of Artificial Intelligence in Building Sign Language Recognition Application

Communication serves as the foundation upon which societies are built, manifesting in various forms such as gestures, sounds, drawing, writing, and speech. However, for individuals with hearing impairments, traditional modes of communication like spoken language can pose substantial challenges. These challenges often result in barriers to effective interaction, not only in personal and social settings but also in educational and professional environments. To bridge this gap, sign language has emerged as an essential and empowering communication tool, enabling individuals with hearing impairments to express themselves with clarity and nuance. Sign language is not just a series of gestures but a fully developed language system. It serves as a vital channel through which individuals with hearing impairments can interact with the world around them, breaking down the barriers that their condition Despite technological advances, the recognition and interpretation of sign language remain complex tasks, especially given the inherent complexity of sign languages characterized by multiple channels, including manual gestures, facial expressions, and body language. This study leverages the Python programming language and the YOLOv8 object detection framework to develop a practical sign language recognition application. This system utilizes deep learning and computer vision to interpret sign language gestures in real-time, aiming to address the limitations of existing recognition systems. The developed system achieved a 95% accuracy rate in recognizing sign language gestures, demonstrating the effectiveness of combining Python with YOLOv8 for this application. This research contributes to the field of assistive technologies by providing a versatile and user-friendly tool that can be deployed across various platforms and environments, ultimately enhancing communication and social integration for individuals with hearing impairments. imposes.