| Autor: |
Candra, Ade, Rosmalinda, Intan, T. Kemala, Purnamasari, Fanindia, Liyanto, Hardy, Nugraha, Abid Tondi, Ewaldo |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 2987 Issue 1, p1-7, 7p |
| Abstrakt: |
The deaf people tend not to get a good education in developing countries. Adults with hearing disabilities also have a high unemployment rate. This condition is triggered by the difficulty of people with speech and hearing impairment in communicating with other people which make it difficult for them to attend lessons, live independently, or have a decent income. Sign language is the main method of communication for people with speech and hearing impairments. Meanwhile, deep learning as a subset of machine learning in the artificial intelligence domain has shown a lot of progress in object detection. Convolutional Neural Network (CNN) is one of the algorithms in deep learning with an excellent ability to recognize objects, and MediaPipe is a machine learning-based framework to solve objects recognition problems such as detecting faces, eyes, hair, hands, gestures, and others. This study aims to develop a machine learning-based sign language translator that can be used by people in Indonesia with speech and hearing impairments to communicate. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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