Abstract
Sign language detection is the process of identifying hand gestures. Aphonic and auditorily impaired people use gestures to communicate with one another, which becomes a hindrance for normal people to understand. The main objective of this paper is to analyze and accurately predict the hand signs and respond in sign, using technology. It deals with the recognition and classification of 26 American sign alphabets and generating sentences from them. Received sentences are analyzed, and a response to the query is generated. MediaPipe was used for the collection of the dataset, the model was trained using a random forest classifier, and the automated response was generated using the RoBERTa model. Broadening this study to include expressions and words may help individuals with special needs communicate with the outside world more quickly and easily while also advancing the development of automated systems that can interpret and use them.
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Sujit, S., Balaraj, A., Kumar, M.S.P., Sagar, A., Anuradha, M. (2024). Sign-Kiosk: A Real-Time Virtual Assistant. In: Joshi, A., Mahmud, M., Ragel, R.G., Kartik, S. (eds) ICT: Cyber Security and Applications. ICTCS 2022. Lecture Notes in Networks and Systems, vol 916. Springer, Singapore. https://doi.org/10.1007/978-981-97-0744-7_10
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DOI: https://doi.org/10.1007/978-981-97-0744-7_10
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