系统梳理120个手语数据集，推动无障碍技术发展
Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards
- 整理35种手语的120个数据资源，构建全面索引
- 发现模态失衡、标注粒度不一等关键问题
- 提供标准化文档模板，适合研究者与开发者使用
手语是聋哑及听力障碍群体使用的丰富视觉语言。尽管手语识别、翻译与生成已取得显著进展，但受限于数据集碎片化、标注不一致和语言覆盖有限。现有基准常无法反映真实交流需求，相关系统性分析仍不足。本文综述了涵盖35种手语的120个手语数据集，分析了模态失衡、标注粒度与签名者偏差等核心挑战，并为未来数据集设计提出建议。我们还提出了包含24个字段的手语数据集说明书（Sign-Language Datasheet），并开源了公共GitHub仓库（https://github.com/Ginqwerty/Open-Sign-Language），以支持标准化文档与可复现评估。本工作为构建面向实际应用的包容性、鲁棒且可扩展的手语技术提供了统一实践基础。
原文摘要 · Abstract (English)
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 (https://github.com/Ginqwerty/Open-Sign-Language) 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.
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