Abstract
Abstract: Sign language is an essential communication tool for India's Deaf and Hard of Hearing people. This study introduces a novel approach for recognising and synthesising Indian Sign Language (ISL) using Long Short-Term Memory (LSTM) networks. LSTM, a kind of recurrent neural network (RNN), has demonstrated promising performance in sequential data processing. In this study, we leverage LSTM to develop a robust ISL recognition system, which can accurately interpret sign gestures in real-time. Additionally, we employ LSTM-based models for ISL synthesis, enabling the conversion of spoken language into sign language for improved inclusivity and accessibility. We evaluate the proposed approach on a diverse dataset of ISL signs, achieving high recognition accuracy and natural sign synthesis. The integration of LSTM in ISL technology holds significant potential for breaking down communication barriers and improving the quality of life for India's deaf and hard of hearing people
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