DESIGN AND IMPLEMENTATION OF SIGN LANGUAGE TRANSLATION USING GESTURE RECOGNITION WITH TENSORFLOW
Abstract
Communication barriers between individuals who use sign language and those who do not, create
challenges in social and professional interactions. This project addresses this issue by developing
a gesture recognition system integrated into a React application to translate physical gestures into
words and sentences. Using TensorFlow and MediaPipe, the model identifies 26 gesture classes
corresponding to the American Sign Language (ASL) alphabet, prioritizing real-time accuracy and
responsiveness. The application aims to enhance accessibility and interaction, providing a
seamless communication bridge through gesture translation. With applications in assistive
technology and human-computer interaction, this project establishes a foundation for advancing
gesture-based systems, fostering inclusivity and innovative interaction methods.
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