dc.contributor.author |
Wijegoonaratna, Sanuka Kumoda |
|
dc.date.accessioned |
2021-06-20T12:07:31Z |
|
dc.date.available |
2021-06-20T12:07:31Z |
|
dc.date.issued |
2020 |
|
dc.identifier.citation |
Wijegoonaratna, Sanuka Kumoda (2020) Realtime Sinhala Sign Language Interpreter Using Hand Gesture Recognition, MSc. Dissertation Informatics Institute of Technology |
en_US |
dc.identifier.other |
2018593 |
|
dc.identifier.uri |
http://dlib.iit.ac.lk/xmlui/handle/123456789/496 |
|
dc.description.abstract |
Sign language is the most famous and common mode of communication for deaf-mute people, to communicate with both normal people and with themselves. This documentation contains a comprehensive research based on Sign Language and build up a tool that will assist a person who is not aware of Sinhala Finger spelled Sign Language to communicate with a hearing impaired person who is aware of Sinhala Sign Language. This documentation presents an overview of the technologies available and discussed in detail. A neural network-based approach used to develop this Realtime Sinhala Sign Language Interpreter tool.
The main aim of this research project is to “Design, implement and evaluate a system which converts the real time captured finger spelled static Sinhala Sign Language sign into readable digital format efficiently”.
This documentation contains the detail description of literature review, methodologies, requirement gathering, design, implementation, testing and evaluation. |
en_US |
dc.subject |
Sinhala Sign Language |
en_US |
dc.subject |
Sign Language Recognition |
en_US |
dc.subject |
Gesture Recognition |
en_US |
dc.subject |
Machine Learning |
en_US |
dc.subject |
Image Processing |
en_US |
dc.title |
Realtime Sinhala Sign Language Interpreter Using Hand Gesture Recognition |
en_US |
dc.type |
Thesis |
en_US |