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.
