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dc.contributor.author Jecee, Mon Mon Jaman
dc.date.accessioned 2022-06-21T06:27:42Z
dc.date.available 2022-06-21T06:27:42Z
dc.date.issued 2022-01-27
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/8246
dc.description.abstract Sign language is the oldest and best form of expressing the language of the mind. Around 466 million people have disabling hearing loss, and 80% of auditory impaired people are illiterate or semi-literate. And most of them exclusively use dactylology to communicate with the world. But most of us do not know dactylology, and interpreters find it very difficult to express that dactylology to mundane people. As a result, we aim to create an authentic-time method for finger spelling-based dactylology based on a neural network. In this work, the designation languages are passed through a filter, and after the filter is applied to the hand gesture, it passes through a process that shows the text of the gesture. This project gives pretty accurate results. en_US
dc.language.iso en_US en_US
dc.publisher ©Daffodil International University en_US
dc.subject Neural networks en_US
dc.subject Neurobiology en_US
dc.subject Sign language en_US
dc.title Voice of Deaf en_US
dc.type Thesis en_US


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