Abstract
Deaf and dumb people use a sign language that can only be communicated through hand gestures to express their ideas and views. This coded language is mainly used by people who have speech and/or hearing impairment. The sign language is constructed by various movement of hands, arms, legs, or facial expressions to express their opinions. Meanings are communicated for every movement or position of gesture. Hand gesture plays a significant role to make mother tongue of impairment people for daily communication. The captured image feature can be extracted to translate the hand gesture communication to text\voice format to minimize the gap between the deaf and normal persons. This work considers the images of sign numerals to classify the numbers 0–9 and the alphabets for A–Z (including space).
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Rajalakshmi, N.R. (2023). Development of Sign Language Recognition Application Using Deep Learning. In: Kumar, R., Pattnaik, P.K., R. S. Tavares, J.M. (eds) Next Generation of Internet of Things. Lecture Notes in Networks and Systems, vol 445. Springer, Singapore. https://doi.org/10.1007/978-981-19-1412-6_24
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DOI: https://doi.org/10.1007/978-981-19-1412-6_24
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