Abstract
The deaf and mute population struggles a lot in expressing their thoughts and ideas to others; Sign Language is the most expressive means of communication for them, but a majority of the general population is callow of sign language, hence the mute and deaf experience difficulties while communicating to the rest of the world. To overcome this communication barrier, a device that can accurately translate sign language gestures to speech and vice-versa in real-time is needed. There exist solutions for converting verbal or written language to sign language in real-time reliably and accurately, however the same cannot be said about translating sign language to textual and/or vocal format. The currently existing systems either do not support communication in both directions, are not real-time, have low recognition accuracy, or require static surrounding conditions. Some systems require additional hardware components like expensive sensors, which tend to increase the cost. In this survey, we have reviewed numerous existing solutions and have categorized them depending on the method used. We hope that the results obtained from this study may serve as a road map to guide future study in the domain of Sign Language Recognition (SLR).
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Pranav, Katarya, R. (2022). A Systematic Study of Sign Language Recognition Systems Employing Machine Learning Algorithms. In: Majhi, S., Prado, R.P.d., Dasanapura Nanjundaiah, C. (eds) Distributed Computing and Optimization Techniques. Lecture Notes in Electrical Engineering, vol 903. Springer, Singapore. https://doi.org/10.1007/978-981-19-2281-7_11
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