Sign Recognition

Sign recognition research aims to automatically interpret sign language, bridging communication gaps for the deaf and hard-of-hearing communities. Current efforts focus on improving accuracy and efficiency using deep learning models like CNNs, RNNs (including GRUs and LSTMs), and graph convolutional networks, often incorporating multi-modal data (e.g., handshape, location, movement, facial expressions) and leveraging techniques like curriculum learning and contrastive learning to enhance performance. These advancements are facilitated by the development of large-scale, annotated datasets and innovative approaches to data augmentation and annotation, leading to improved sign recognition systems and enabling new applications in areas such as assistive technologies and digital accessibility.