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An Interactive Sign Language Translator
Abstract
The chapter proposes a system that offers a groundbreaking solution to translate sign language into text and speech. This is achieved by implementing a Convolutional Neural Network (CNN) that effectively recognizes hand gestures from images. The process begins with detecting and processing hand movements from the webcam image using the Mediapipe library. Subsequently, the interpreted data is passed through a classifier to predict hand movements accurately. The resulting model has the potential to significantly bridge the communication gap between Deaf/Dumb individuals and the general hearing population. By enabling more effective expression and understanding of sign language, this innovative system prioritizes the perspective and preferences of the patient, bolstering inclusive communication practices.
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