About this document
Real-Time Dynamic Sign Language Recognition by Lamiss Kara is a document available to read on EtoBox.
This document presents a study on dynamic sign language recognition using real-time videos, specifically focusing on Saudi sign language. The authors developed a dataset of 3,454 videos to train a convolutional long short-term memory (convLSTM) model, aiming to facilitate communication for the deaf and hard of hearing. The model achieved 70% accuracy in recognizing selected health-related signs, demonstrating its potential to reduce isolation among deaf individuals.
- Author
- Lamiss Kara
- Language
- EN