Sign Language Recognition and Translation for Low-Resource Languages: Challenges and Pathways Forward
For researchers and practitioners working on sign language technology for under-resourced languages, this paper offers a structured analysis and actionable lessons, but it is a review without empirical results.
This systematic review identifies challenges in sign language recognition for low-resource languages, using Azerbaijan Sign Language as a case study, and proposes three paradigm shifts (data-centric AI, signer-adaptive systems, task-specific metrics) along with a technical roadmap. No concrete performance numbers are provided.
Sign languages are natural, visual-gestural languages used by Deaf communities worldwide. Over 300 distinct sign languages remain severely low-resource due to limited documentation, sparse datasets, and insufficient computational tools. This systematic review synthesizes literature on sign language recognition and translation for under-resourced languages, using Azerbaijan Sign Language (AzSL) as a case study. Analysis of global initiatives extracts eight actionable lessons, including community co-design, dialectal diversity capture, and privacy-preserving pose-based representations. Turkic sign languages (Kazakh, Turkish, Azerbaijani) receive special attention, as linguistic proximity enables effective transfer learning. We propose three paradigm shifts: from architecture-centric to data-centric AI, from signer-independent to signer-adaptive systems, and from reference-based to task-specific evaluation metrics. A technical roadmap for AzSL leverages lightweight MediaPipe-based architectures, community-validated annotations, and offline-first deployment. Progress requires sustained interdisciplinary collaboration centered on Deaf communities to ensure cultural authenticity, ethical governance, and practical communication benefit.