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The Sign Language Interpreter project uses a CNN model to predict letters from images or webcam input, enhancing communication for the deaf and hard of hearing. It features an intuitive interface built with Streamlit, supported by a robust backend of Python, FastAPI, and Jupyter Notebook, promoting inclusivity through innovative technology.

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The Sign Language Interpreter project enhances communication between individuals who are deaf or hard of hearing and those who use spoken language. Utilizing a Convolutional Neural Network (CNN) model, this project predicts letters from images or webcam input, offering an intuitive interface. Streamlit ensures a seamless frontend experience, while Python, FastAPI, and Jupyter Notebook form the robust backend. This initiative promotes inclusivity and exemplifies the integration of innovative machine learning techniques with user-centric design.

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The Sign Language Interpreter project uses a CNN model to predict letters from images or webcam input, enhancing communication for the deaf and hard of hearing. It features an intuitive interface built with Streamlit, supported by a robust backend of Python, FastAPI, and Jupyter Notebook, promoting inclusivity through innovative technology.

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