The Sign Language Sentence Translator is an intelligent system designed to improve communication between deaf and mute individuals and the general public. Since sign language is not widely understood, it often creates barriers in daily interactions. This project introduces a real-time solution that translates continuous sign language gestures into clear text and speech using advanced technologies. The system captures live video through a camera and processes it into individual frames. Hand landmarks are detected using MediaPipe, and these features are analyzed using deep learning models such as Convolutional Neural Networks (CNNs) for extracting patterns and Long Short-Term Memory (LSTM) networks for understanding gesture sequences over time. The resulting output is further refined using Natural Language Processing (NLP) techniques to form meaningful and grammatically correct sentences. Unlike traditional approaches that only identify individual gestures, this system is designed to interpret continuous sign sequences and form complete sentences. It also includes support for multiple languages and converts text into speech, which increases its usability and flexibility. By focusing on accessibility, this project supports the development of inclusive technologies and helps bridge communication gaps.
Vaidya et al. (Thu,) studied this question.
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