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Sign Language Reader Using LSTM

Sherin Susan Varghese, Meenu Elsa Thomas, Naina Nabisath S, Namitha Gireesh, Revathy N

Abstract


This study presents a novel approach to dynamic sign language recognition (DSLR) using Long Short-Term Memory (LSTM) networks. The system converts dynamic sign language gestures into audio representations, utilizing data pre-processing, feature extraction, LSTM-based sequence modeling, and audio synthesis. Sign language videos are collected and preprocessed to extract meaningful features, which are fed into LSTM network architecture. The LSTM model captures sequential patterns, enabling accurate recognition of individual signs and continuous sentences. The audio synthesis module generates real-time auditory feedback. This work contributes to advancing assistive technologies for the deaf community.


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References


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