A Comprehensive Deep Learning Based System for Real Time Sign Language Recognition and Translation Using Raspberry Pi

Sign language is an important aspect of human communication for a variety of reasons, particularly when deaf and dumb individuals are communicating. This study describes a novel method for translating sign language into spoken language that employs a Raspberry Pi 3 and the MobileNet-V2 deep learning model. Technology has advanced significantly, and many studies have been conducted to assist the deaf and dumb. Deep learning and computer vision can also be utilized to support the cause and have an impact on it. The system includes a camera that collects images of the signer's hand gestures and processes them for classification using the MobileNet V2 model. The translated text is entered into text-to-speech software. The system was trained on a huge dataset of sign language movements using transfer learning techniques, and it attained an accuracy of 99.52% on the validation set. The Raspberry Pi 3 was chosen as the hardware platform for its low cost, portability, and suitability for various applications and environments.