Sign Language Datasets

The field creates resources for studying sign languages across recognition, translation, fingerspelling, emotion, and conversation, with annotations ranging from glosses and text to pose, motion, and facial cues. Researchers combine expert and community annotation with automatic or LLM-assisted labeling, weak supervision from transcripts, and synthetic augmentation to expand coverage across languages and recording settings. Benchmarks increasingly test semantic understanding and specific linguistic cues, not only recognition accuracy or translation overlap. A central challenge is balancing scarce, costly-to-annotate data with linguistic and signer diversity, while using signer-independent, leakage-resistant evaluations that reflect real-world generalization.