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

arXiv: https://arxiv.org/abs/2606.19352 · HTML: https://mlsanity.com/paper/2606.19352/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 51 of its 134 references that are in the map.
- First posted on arXiv 28 Apr 2026.

## Builds on (most cited first, 30 of 51)

- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [Improving Sign Language Translation with Monolingual Data by Sign Back-Translation](https://mlsanity.com/paper/2105.12397/) (2021)
- [How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language](https://mlsanity.com/paper/2008.08143/) (2020)
- [Two-Stream Network for Sign Language Recognition and Translation](https://mlsanity.com/paper/2211.01367/) (2022)
- [Progressive Transformers for End-to-End Sign Language Production](https://mlsanity.com/paper/2004.14874/) (2020)
- [MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language](https://mlsanity.com/paper/1812.01053/) (2018)
- [BSL-1K: Scaling up co-articulated sign language recognition using mouthing cues](https://mlsanity.com/paper/2007.12131/) (2020)
- [Gloss-free Sign Language Translation: Improving from Visual-Language Pretraining](https://mlsanity.com/paper/2307.14768/) (2023)
- [Video-based Sign Language Recognition without Temporal Segmentation](https://mlsanity.com/paper/1801.10111/) (2018)
- [LLMs are Good Sign Language Translators](https://mlsanity.com/paper/2404.00925/) (2024)
- [AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methods](https://mlsanity.com/paper/2008.00932/) (2020)
- [YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus](https://mlsanity.com/paper/2306.15162/) (2023)
- [Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production](https://mlsanity.com/paper/2203.15354/) (2022)
- [SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding](https://mlsanity.com/paper/2305.04868/) (2023)
- [Adversarial Training for Multi-Channel Sign Language Production](https://mlsanity.com/paper/2008.12405/) (2020)
- [Uni-Sign: Toward Unified Sign Language Understanding at Scale](https://mlsanity.com/paper/2501.15187/) (2025)
- [Towards Privacy-Aware Sign Language Translation at Scale](https://mlsanity.com/paper/2402.09611/) (2024)
- [Global-local Enhancement Network for NMFs-aware Sign Language Recognition](https://mlsanity.com/paper/2008.10428/) (2020)
- [Watch, read and lookup: learning to spot signs from multiple supervisors](https://mlsanity.com/paper/2010.04002/) (2020)
- [Content4All Open Research Sign Language Translation Datasets](https://mlsanity.com/paper/2105.02351/) (2021)
- [T2S-GPT: Dynamic Vector Quantization for Autoregressive Sign Language Production from Text](https://mlsanity.com/paper/2406.07119/) (2024)
- [Fingerspelling recognition in the wild with iterative visual attention](https://mlsanity.com/paper/1908.10546/) (2019)
- [Improving Continuous Sign Language Recognition with Cross-Lingual Signs](https://mlsanity.com/paper/2308.10809/) (2023)
- [Lost in Translation, Found in Context: Sign Language Translation with Contextual Cues](https://mlsanity.com/paper/2501.09754/) (2025)
- [Conditional Variational Autoencoder for Sign Language Translation with Cross-Modal Alignment](https://mlsanity.com/paper/2312.15645/) (2023)
- [American Sign Language fingerspelling recognition in the wild](https://mlsanity.com/paper/1810.11438/) (2018)
- [Sign Language Translation with Iterative Prototype](https://mlsanity.com/paper/2308.12191/) (2023)
- [BosphorusSign22k Sign Language Recognition Dataset](https://mlsanity.com/paper/2004.01283/) (2020)
- [A Simple Baseline for Spoken Language to Sign Language Translation with 3D Avatars](https://mlsanity.com/paper/2401.04730/) (2024)

## Abstract (arXiv)

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.