Work

Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards

Year: 2026

Type: conference-paper

Abstract: Sign languages are expressive visual languages used by Deaf and Hard-of-Hearing (DHH) communities.Despite substantial progress in sign-language recognition, translation, and production, advances remain constrained by fragmented datasets, inconsistent annotations, and limited linguistic coverage.Existing benchmarks often fail to reflect real-world communication needs, and systematic analyses of these limitations remain limited.In this survey, we present a comprehensive index of sign-language datasets, covering 120 resources across 35 sign languages.We analyze key challenges such as modality imbalance, annotation granularity, and signer bias, and outline considerations for future dataset design.We also introduce a 24-field Sign-Language Datasheet and release a public GitHub repository 1 to support standardized documentation and reproducible evaluation.Overall, our work provides a unified and practical foundation for developing inclusive, robust, and scalable sign-language technologies in real-world applications.

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/2026.acl-long.1928

Authors: Yiming Ni, Zhi-Qi Cheng, Jiayu Li, Wei Cheng

Institutions: University of Washington, University of Washington Tacoma

Language: English

Topic: Hand Gesture Recognition Systems

Subfield: Human-Computer Interaction

Field: Computer Science

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: hybrid

Funder: University of Washington