AI Based Sign Language to Text Converter

Communication between hearing-impaired individuals and the general public is often limited due to the lack of widespread knowledge of sign language. Recent advancements in artificial intelligence have enabled the development of gesture-recognition technologies that can help overcome this gap by translating hand signs into readable text. In this work, an AI-based, real-time sign language conversion system is proposed to recognize hand gestures and map them to their corresponding textual meanings. The design of the system follows a structured A systematic process that involves collecting data, segmentation, data cleaning and preparation, identification of key features, and training of the model gesture recognition. Each stage contributes to refining the captured gesture data, ensuring that the model learns clear and meaningful patterns for accurate prediction. The goal of the proposed system is to offer a fast, dependable, and accessible communication tool that supports hearing- and speech-impaired individuals in public, educational, and assistive environments. By efficiently interpreting commonly used signs, the system can significantly enhance interaction and reduce communication barriers. Experimental results validate that the model performs reliably in real-time scenarios, demonstrating its capability to deliver consistent outcomes. Overall, this AI-driven promoting inclusive communication and contributing to the development of assistive technologies that enhance social participation for individuals with communication challenges.