Abstract
Sign language is learned by deaf and dumb, and usually, it is not known to normal people, so it becomes a challenge for communication between a normal and hearing-impaired person. Thus, we decided to bridge the gap between hearing impaired and normal people and make the conversation easier. Sign language is one of the oldest and most natural forms of language for communication, but since most people do not know sign language and interpreters are very difficult to come by we have come up with a real-time method using neural networks for finger-spelling-based American sign language. In this research, a hand image is first passed through a filter and after the filter has applied the image is passed through a classifier that predicts the class of the hand gesture. Our method provides above 98% accuracy for the 26 letters of the alphabet. The paper implements that a functional real-time vision-based American sign language recognition for D&M people has been developed for ASL alphabets.
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Kumar, A., Kumar, S., Singh, S., Jha, V. (2022). Sign Language Recognition Using Convolutional Neural Network. In: Fong, S., Dey, N., Joshi, A. (eds) ICT Analysis and Applications. Lecture Notes in Networks and Systems, vol 314. Springer, Singapore. https://doi.org/10.1007/978-981-16-5655-2_87
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DOI: https://doi.org/10.1007/978-981-16-5655-2_87
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