Publication:
IoT Based Sign Language Recognition System

dc.contributor.authorPunsara, K.K.T.
dc.contributor.authorPremachandra, H.H.R.C.
dc.contributor.authorChanaka, A.W.A.D.
dc.contributor.authorWijayawickrama, R.V.
dc.contributor.authorAbhayasinghe, N.
dc.contributor.authorDe Silva, R.
dc.date.accessioned2022-02-25T10:00:33Z
dc.date.available2022-02-25T10:00:33Z
dc.date.issued2020-12-10
dc.description.abstractSign language is the key communication medium, which deaf and mute people use in their day - to-day life. Talking to disabled people will cause a difficult situation since a non-mute person cannot understand their hand gestures and in many instances, mute people are hearing impaired. Same as Sinhala, Tamil, English, or any other language, sign language also tend to have differences according to the region. This paper is an attempt to assist deaf and mute people to develop an effective communication mechanism with non-mute people. The end product of this project is a combination of a mobile application that can translate the sign language into digital voice and loT-enabled, light-weighted wearable glove, which capable of recognizing twenty-six English alphabet, digits, and words. Better user experience provides with voice-to-text feature in mobile application to reduce the communication gap within mute and non-mute communities. Research findings and results from the current system visualize the output of the product can be optimized up to 25 % -35 % with an enhanced pattern recognition mechanism.en_US
dc.identifier.doi10.1109/ICAC51239.2020.9357267en_US
dc.identifier.isbn978-1-7281-8412-8
dc.identifier.urihttps://rda.sliit.lk/handle/123456789/1412
dc.language.isoenen_US
dc.publisher2020 2nd International Conference on Advancements in Computing (ICAC), SLIITen_US
dc.relation.ispartofseriesVol.1;
dc.subjectsign languageen_US
dc.subjectInternet of Thingsen_US
dc.subjectGesture recognitionen_US
dc.subjectSmart gloveen_US
dc.subjectRecurrent neural networken_US
dc.titleIoT Based Sign Language Recognition Systemen_US
dc.typeArticleen_US
dspace.entity.typePublication

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