Isolated Sign Language Recognition

Isolated Sign Language Recognition (ISLR) aims to automatically identify individual signs from video, bridging communication gaps for the deaf community. Current research heavily utilizes deep learning, employing architectures like transformers, convolutional neural networks (CNNs), and graph attention networks, often incorporating spatio-temporal features extracted from skeletal data or RGB images. These models are being enhanced through techniques such as transfer learning across sign languages and self-supervised learning to address data scarcity issues in many sign languages. Advances in ISLR are crucial for developing practical applications like sign language translation tools, educational resources, and accessible communication technologies.