A Machine Learning Model for converting Visual Indian Sign Language to Text

Good communication is essential in a world where connections are becoming more and more blurred. But traditional communication barriers are magnified for the community of hearing-impaired people, highlighting the need for creative solutions. With the use of cutting-edge technologies like Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and Multimodal Spatio-Temporal Graph Neural Networks (MST-GNN), this project presents a novel Indian Sign Language Recognition System. By accurately interpreting and comprehending Indian Sign Language (ISL) gestures in real-time, our system acts as a bridge. We have developed an inclusive communication platform by applying state-of-the-art algorithms, preprocessing, and careful data collection. This research represents a dedication to promoting inclusivity and understanding for everyone, regardless of their hearing ability, and it also demonstrates the potential of contemporary technology to enhance accessibility. By harnessing LSTM, CNNs, and MST-GNN, our system transforms the way we communicate, fostering a more inclusive society.