Abstract
By translating hand gestures into a series of words or speech, sign language recognition (SLR) aims to improve communication between deaf and dumb people and the general public. Despite the fact that this activity has a significant societal impact, it is nevertheless challenging due to the complexity and wide variation of hand behavior. Existing SLR techniques use hand-composed pieces to characterize sign language movement and develop division models based on those aspects. It's difficult to create reliable features that respond to a wide range of hand movements. The goal of this project is to develop a sign language recognition project using CNN and predict the output in the form of text and audio.
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Pandey, S. (2023). Automated Gesture Recognition and Speech Conversion Tool for Speech Impaired. In: Reddy, A.B., Nagini, S., Balas, V.E., Raju, K.S. (eds) Proceedings of Third International Conference on Advances in Computer Engineering and Communication Systems. Lecture Notes in Networks and Systems, vol 612. Springer, Singapore. https://doi.org/10.1007/978-981-19-9228-5_39
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DOI: https://doi.org/10.1007/978-981-19-9228-5_39
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