Sign language processing advances rapidly for high-resource languages such as American Sign Language (ASL), yet most of the world’s sign languages lack the phonological annotations new methods require. We present the first zero-shot cross-lingual framework for handshape recognition, transferring from ASL to Catalan Sign Language (LSC). Our approach leverages the decomposition of handshapes into five phonological features — selected fingers, flexion, spread, thumb position, and thumb contact — shared across both languages, to decode LSC handshapes from predicted features via a composite phonological distance metric. We evaluate three architectures (MLP, SL-GCN, SHuBERT) trained on two ASL corpora (PopSign, Sem-Lex) against a 37-handshape, single-signer LSC benchmark. Zero-shot transfer proves viable once recording-format disparities are harmonized, reaching 80.0% phonological feature accuracy and 54.5% expected handshape accuracy. Phonological decomposition thus offers a bridge for extending sign language technologies to low-resource languages without any target-language video training labels.

This article introduces the VISTA-SL project, which aims to create an integrated e-learning platform for four European sign languages: German Sign Language, Greek Sign Language, Irish Sign Language, and Dutch Sign Language. Designed as a complement to face-to-face classes, the VISTA-SL platform will combine expertise in sign language education and education technologies to provide an adaptive and interactive learning environment suitable for deaf, hard of hearing and hearing users seeking to learn a sign language, whether it constitutes their first language or not. Building on a co-ordinated curriculum that covers vocabulary, grammar and Deaf culture materials, the platform will provide video material presented by deaf L1 signers, together with games and gamification features to motivate learning, while also providing several assistive technologies. By leveraging cutting edge language processing and computer vision approaches, the platform will provide augmented reality feedback, 3D avatars and an LLM-based virtual instructor, as part of the learning environment. VISTA-SL is developed in collaboration with end-user focus groups, comprising deaf, hard of hearing and hearing individuals. This will serve to ensure that the educational platform aligns with the expectations and needs of its intended users.