Continuous Sign Language

Continuous sign language recognition (CSLR) aims to automatically translate continuous sign language gestures into text, facilitating communication for the deaf and hard-of-hearing. Current research heavily focuses on improving model robustness and efficiency, employing deep learning architectures like transformers and convolutional neural networks, often incorporating attention mechanisms and techniques such as knowledge distillation and temporal super-resolution to handle complex backgrounds and improve real-time performance. These advancements are crucial for developing practical, reliable CSLR systems applicable in diverse real-world settings, bridging communication gaps and enhancing accessibility.