# Quantitative Survey of the State of the Art in Sign Language Recognition

arXiv: https://arxiv.org/abs/2008.09918 · HTML: https://mlsanity.com/paper/2008.09918/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2020.

## Built on by (most cited first, 30 of 50)

- [Two-Stream Network for Sign Language Recognition and Translation](https://mlsanity.com/paper/2211.01367/) (2022)
- [Including Signed Languages in Natural Language Processing](https://mlsanity.com/paper/2105.05222/) (2021)
- [Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production](https://mlsanity.com/paper/2203.15354/) (2022)
- [SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognition](https://mlsanity.com/paper/2110.05382/) (2021)
- [SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding](https://mlsanity.com/paper/2305.04868/) (2023)
- [Mixed SIGNals: Sign Language Production via a Mixture of Motion Primitives](https://mlsanity.com/paper/2107.11317/) (2021)
- [Stochastic Transformer Networks with Linear Competing Units: Application to end-to-end SL Translation](https://mlsanity.com/paper/2109.13318/) (2021)
- [ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition](https://mlsanity.com/paper/2304.05934/) (2023)
- [OpenHands: Making Sign Language Recognition Accessible with Pose-based Pretrained Models across Languages](https://mlsanity.com/paper/2110.05877/) (2021)
- [Content4All Open Research Sign Language Translation Datasets](https://mlsanity.com/paper/2105.02351/) (2021)
- [Aligning Subtitles in Sign Language Videos](https://mlsanity.com/paper/2105.02877/) (2021)
- [SignDiff: Diffusion Model for American Sign Language Production](https://mlsanity.com/paper/2308.16082/) (2023)
- [Sign Language Video Retrieval with Free-Form Textual Queries](https://mlsanity.com/paper/2201.02495/) (2022)
- [Scaling up Multimodal Pre-training for Sign Language Understanding](https://mlsanity.com/paper/2408.08544/) (2024)
- [Automatic dense annotation of large-vocabulary sign language videos](https://mlsanity.com/paper/2208.02802/) (2022)
- [Discrete to Continuous: Generating Smooth Transition Poses from Sign Language Observation](https://mlsanity.com/paper/2411.16810/) (2024)
- [Sign-IDD: Iconicity Disentangled Diffusion for Sign Language Production](https://mlsanity.com/paper/2412.13609/) (2024)
- [MASA: Motion-aware Masked Autoencoder with Semantic Alignment for Sign Language Recognition](https://mlsanity.com/paper/2405.20666/) (2024)
- [Using Motion History Images with 3D Convolutional Networks in Isolated Sign Language Recognition](https://mlsanity.com/paper/2110.12396/) (2021)
- [Improving Continuous Sign Language Recognition with Consistency Constraints and Signer Removal](https://mlsanity.com/paper/2212.13023/) (2022)
- [Sign Segmentation with Changepoint-Modulated Pseudo-Labelling](https://mlsanity.com/paper/2104.13817/) (2021)
- [ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research](https://mlsanity.com/paper/2105.05066/) (2021)
- [Human Part-wise 3D Motion Context Learning for Sign Language Recognition](https://mlsanity.com/paper/2308.09305/) (2023)
- [A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities](https://mlsanity.com/paper/2408.05436/) (2024)
- [ArabSign: A Multi-modality Dataset and Benchmark for Continuous Arabic Sign Language Recognition](https://mlsanity.com/paper/2210.03951/) (2022)
- [WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language](https://mlsanity.com/paper/2203.06096/) (2022)
- [Self-Supervised Representation Learning with Spatial-Temporal Consistency for Sign Language Recognition](https://mlsanity.com/paper/2406.10501/) (2024)
- [LSA-T: The first continuous Argentinian Sign Language dataset for Sign Language Translation](https://mlsanity.com/paper/2211.15481/) (2022)
- [TCNet: Continuous Sign Language Recognition from Trajectories and Correlated Regions](https://mlsanity.com/paper/2403.11818/) (2024)
- [SignX: Continuous Sign Recognition in Compact Pose-Rich Latent Space](https://mlsanity.com/paper/2504.16315/) (2025)

## Abstract (arXiv)

This work presents a meta study covering around 300 published sign language recognition papers with over 400 experimental results. It includes most papers between the start of the field in 1983 and 2020. Additionally, it covers a fine-grained analysis on over 25 studies that have compared their recognition approaches on RWTH-PHOENIX-Weather 2014, the standard benchmark task of the field. Research in the domain of sign language recognition has progressed significantly in the last decade, reaching a point where the task attracts much more attention than ever before. This study compiles the state of the art in a concise way to help advance the field and reveal open questions. Moreover, all of this meta study's source data is made public, easing future work with it and further expansion. The analyzed papers have been manually labeled with a set of categories. The data reveals many insights, such as, among others, shifts in the field from intrusive to non-intrusive capturing, from local to global features and the lack of non-manual parameters included in medium and larger vocabulary recognition systems. Surprisingly, RWTH-PHOENIX-Weather with a vocabulary of 1080 signs represents the only resource for large vocabulary continuous sign language recognition benchmarking world wide.