| Autor: |
Nanditha, Ravindran, Rini, Ramalingam, V. V. |
| Předmět: |
|
| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3075 Issue 1, p1-5, 5p |
| Abstrakt: |
In order to communicate because they are unable to do so through hearing or speech, speech-impaired people must utilize sign language. Although sign language is used by all people with speech impairments, those who are not proficient in it find it difficult to communicate with those who are. As a result, those who have speech problems really need a sign language interpreter. Both informal and formal communication is difficult for them because of this. Motion detection and gesture identification have both benefited from recent advances in deep learning. Also, there has been a significant advancement in computer vision that will make it simple for us to track hand motions. The suggested method makes an effort to instantly translate hand gestures into corresponding English. Using video, this technology converts hand gestures into writing that non-signers may understand. CNN will be utilized to categorize hand gestures. This method bridges the communication gap between signers and non-signers. Those with speech impairments will find it simpler to communicate as a result. With the help of the technology, the final user will be able to comprehend sign language. Optical character recognition benefits from the emerging subject of machine learning. With the aid of this technology, the disabled will be able to communicate with one another in real time sign language recognition. Communication is never easy with someone who has hearing loss. Unquestionably, sign language has become the best option for people with hearing and speech impairments since it allows them to communicate their thoughts and feelings to others in a highly efficient way. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
