Beyond ASL: AI for Low-Resource Sign Languages A Research Agenda for Indian and African Contexts

Abstract

There are over 300 sign languages in active use worldwide, serving an estimated 70 million Deaf signers. Despite this, the overwhelming majority of AI research on sign languages targets American Sign Language (ASL), with smaller but growing efforts on German, British, Chinese, and Indian Sign Language. Regional Indian variants and African sign languages remain almost entirely unaddressed. This paper argues that closing this gap is not, as commonly framed, a question of building more datasets in the same paradigm. We make three claims. First, the term "low-resource" means something structurally different for sign languages than for spoken languages — sign languages are not signed versions of spoken languages, they have no standardised writing system, they are multimodal, and within-country dialectal variation is unusually high — and naive transfer of low-resource spoken-NLP techniques produces misleading evaluations and brittle systems. Second, four bottlenecks (data scarcity, inappropriate benchmarks, the limits of cross-language transfer, and structural exclusion of the Deaf community from research design) compound rather than substitute for each other; addressing only one will not produce usable systems. Third, a Deaf-led research agenda focused on Indian and African sign languages is both ethically necessary and scientifically productive — these contexts surface problems that ASL-centric research has been able to ignore. We propose specific methodological commitments, six concrete research questions, and a strategic case for why Indian Sign Language should serve as a reference setting for the next phase of work.

About the author

Pranay Mahendrakar is an ai specialist and large language model engineer based in Bengaluru, India. He builds production artificial intelligence systems and publishes open-access research on how those systems fail. See all 70 papers by Pranay Mahendrakar, or his ORCID record.