# A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets

arXiv: https://arxiv.org/abs/2204.03328 · HTML: https://mlsanity.com/paper/2204.03328/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 156 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 30 of 39)

- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [DeepPose: Human Pose Estimation via Deep Neural Networks](https://mlsanity.com/paper/1312.4659/) (2013)
- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [Learning to Estimate 3D Hand Pose from Single RGB Images](https://mlsanity.com/paper/1705.01389/) (2017)
- [GANerated Hands for Real-time 3D Hand Tracking from Monocular RGB](https://mlsanity.com/paper/1712.01057/) (2017)
- [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)
- [V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map](https://mlsanity.com/paper/1711.07399/) (2017)
- [Skeleton Aware Multi-modal Sign Language Recognition](https://mlsanity.com/paper/2103.08833/) (2021)
- [Cross-modal Deep Variational Hand Pose Estimation](https://mlsanity.com/paper/1803.11404/) (2018)
- [Visual Alignment Constraint for Continuous Sign Language Recognition](https://mlsanity.com/paper/2104.02330/) (2021)
- [AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methods](https://mlsanity.com/paper/2008.00932/) (2020)
- [TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation](https://mlsanity.com/paper/2010.05468/) (2020)
- [Spatial-Temporal Multi-Cue Network for Continuous Sign Language Recognition](https://mlsanity.com/paper/2002.03187/) (2020)
- [Neural Sign Language Translation based on Human Keypoint Estimation](https://mlsanity.com/paper/1811.11436/) (2018)
- [Transferring Cross-domain Knowledge for Video Sign Language Recognition](https://mlsanity.com/paper/2003.03703/) (2020)
- [BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis](https://mlsanity.com/paper/1704.02612/) (2017)
- [Global-local Enhancement Network for NMFs-aware Sign Language Recognition](https://mlsanity.com/paper/2008.10428/) (2020)
- [Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention](https://mlsanity.com/paper/1907.08871/) (2019)
- [Hands Deep in Deep Learning for Hand Pose Estimation](https://mlsanity.com/paper/1502.06807/) (2015)
- [BosphorusSign22k Sign Language Recognition Dataset](https://mlsanity.com/paper/2004.01283/) (2020)
- [Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble](https://mlsanity.com/paper/2110.06161/) (2021)
- [Augmented Skeleton Space Transfer for Depth-based Hand Pose Estimation](https://mlsanity.com/paper/1805.04497/) (2018)
- [Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose Estimation](https://mlsanity.com/paper/1708.03416/) (2017)
- [Towards Good Practices for Deep 3D Hand Pose Estimation](https://mlsanity.com/paper/1707.07248/) (2017)
- [DRPose3D: Depth Ranking in 3D Human Pose Estimation](https://mlsanity.com/paper/1805.08973/) (2018)
- [Score-level Multi Cue Fusion for Sign Language Recognition](https://mlsanity.com/paper/2009.14139/) (2020)
- [Efficiently Creating 3D Training Data for Fine Hand Pose Estimation](https://mlsanity.com/paper/1605.03389/) (2016)
- [3D human pose estimation from depth maps using a deep combination of poses](https://mlsanity.com/paper/1807.05389/) (2018)

## Built on by (most cited first, 5 of 5)

- [A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation](https://mlsanity.com/paper/2308.08849/) (2023)
- [Sign Language Recognition Based On Facial Expression and Hand Skeleton](https://mlsanity.com/paper/2407.02241/) (2024)
- [Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks](https://mlsanity.com/paper/2506.04367/) (2025)
- [Live American Sign Language Letter Classification with Convolutional Neural Networks](https://mlsanity.com/paper/2305.17192/) (2023)
- [Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards](https://mlsanity.com/paper/2606.19352/) (2026)

## Abstract (arXiv)

A machine can understand human activities, and the meaning of signs can help overcome the communication barriers between the inaudible and ordinary people. Sign Language Recognition (SLR) is a fascinating research area and a crucial task concerning computer vision and pattern recognition. Recently, SLR usage has increased in many applications, but the environment, background image resolution, modalities, and datasets affect the performance a lot. Many researchers have been striving to carry out generic real-time SLR models. This review paper facilitates a comprehensive overview of SLR and discusses the needs, challenges, and problems associated with SLR. We study related works about manual and non-manual, various modalities, and datasets. Research progress and existing state-of-the-art SLR models over the past decade have been reviewed. Finally, we find the research gap and limitations in this domain and suggest future directions. This review paper will be helpful for readers and researchers to get complete guidance about SLR and the progressive design of the state-of-the-art SLR model