Abstract
A condition in which an individual has trouble forming sounds is called speech impairment, and a condition in which an individual cannot completely receive sounds through their ears is called hearing impairment. Both of these impairments affect an individual’s ability to communicate with others. Those affected by these impairments use alternative forms of communication, such as sign language. However, it is difficult for non-sign language speakers to communicate with sign language speakers. This is because most of the sign language recognition solutions are not lightweight or portable and require considerable hardware like a computer for usage. This work focuses on developing a computer vision-based application that translates sign language into text using a Convolutional Neural Network, thus enabling signers and non-signers to communicate. This application will support mobile use and work without an internet connection as well, thus serving as a ubiquitous communication aid.
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Gandhi, M., Shah, P., Solanki, D., Shete, P. (2021). Sign Language Recognition Using Convolutional Neural Network. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1441. Springer, Cham. https://doi.org/10.1007/978-3-030-88244-0_27
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DOI: https://doi.org/10.1007/978-3-030-88244-0_27
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