CASL-W60: A word-level dataset for central African sign language recognition

Sign language is a non-verbal discourse system used by people who are hard of hearing. It also carries cultural context and regional constructs, enabling meaningful communication and often preserving unique traditions. In the Central African region, local sign languages have distinct linguistic constructs but remain underrepresented in the literature, creating a significant gap in regional word-level datasets for machine learning practitioners. In this research, we present a dataset (CASL-W60) comprising 60 word-level Central African sign language (CASL), collected from 19 volunteers. Each word contains 10-12 video samples per signer, captured following standard African sign language video references. The dataset comprises MP4 video files that are systematically organized and made available through an online repository. We demonstrate its applicability through word-level classification of the 60 sign words. This dataset serves as a valuable resource for developing various applications, including sign language translation, sentence recognition or generation from word-level signs, and sign gloss detection.