# Neural Sign Language Translation based on Human Keypoint Estimation

arXiv: https://arxiv.org/abs/1811.11436 · HTML: https://mlsanity.com/paper/1811.11436/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2018.

## Built on by (most cited first, 30 of 65)

- [Two-Stream Network for Sign Language Recognition and Translation](https://mlsanity.com/paper/2211.01367/) (2022)
- [LLMs are Good Sign Language Translators](https://mlsanity.com/paper/2404.00925/) (2024)
- [YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus](https://mlsanity.com/paper/2306.15162/) (2023)
- [Open-Domain Sign Language Translation Learned from Online Video](https://mlsanity.com/paper/2205.12870/) (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)
- [Uni-Sign: Toward Unified Sign Language Understanding at Scale](https://mlsanity.com/paper/2501.15187/) (2025)
- [Gloss Attention for Gloss-free Sign Language Translation](https://mlsanity.com/paper/2307.07361/) (2023)
- [Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers and Mixture Density Networks](https://mlsanity.com/paper/2103.06982/) (2021)
- [Mixed SIGNals: Sign Language Production via a Mixture of Motion Primitives](https://mlsanity.com/paper/2107.11317/) (2021)
- [Sign Language Translation from Instructional Videos](https://mlsanity.com/paper/2304.06371/) (2023)
- [YouTube-SL-25: A Large-Scale, Open-Domain Multilingual Sign Language Parallel Corpus](https://mlsanity.com/paper/2407.11144/) (2024)
- [Read and Attend: Temporal Localisation in Sign Language Videos](https://mlsanity.com/paper/2103.16481/) (2021)
- [Gloss-Free End-to-End Sign Language Translation](https://mlsanity.com/paper/2305.12876/) (2023)
- [T2S-GPT: Dynamic Vector Quantization for Autoregressive Sign Language Production from Text](https://mlsanity.com/paper/2406.07119/) (2024)
- [BBC-Oxford British Sign Language Dataset](https://mlsanity.com/paper/2111.03635/) (2021)
- [SimulSLT: End-to-End Simultaneous Sign Language Translation](https://mlsanity.com/paper/2112.04228/) (2021)
- [Sign Language Production: A Review](https://mlsanity.com/paper/2103.15910/) (2021)
- [Sign Language Translation with Hierarchical Spatio-TemporalGraph Neural Network](https://mlsanity.com/paper/2111.07258/) (2021)
- [Evaluating the Immediate Applicability of Pose Estimation for Sign Language Recognition](https://mlsanity.com/paper/2104.10166/) (2021)
- [SignDiff: Diffusion Model for American Sign Language Production](https://mlsanity.com/paper/2308.16082/) (2023)
- [Machine Translation from Signed to Spoken Languages: State of the Art and Challenges](https://mlsanity.com/paper/2202.03086/) (2022)
- [Machine Translation between Spoken Languages and Signed Languages Represented in SignWriting](https://mlsanity.com/paper/2210.05404/) (2022)
- [JWSign: A Highly Multilingual Corpus of Bible Translations for more Diversity in Sign Language Processing](https://mlsanity.com/paper/2311.10174/) (2023)
- [A Tale of Two Languages: Large-Vocabulary Continuous Sign Language Recognition from Spoken Language Supervision](https://mlsanity.com/paper/2405.10266/) (2024)
- [Bridging Sign and Spoken Languages: Pseudo Gloss Generation for Sign Language Translation](https://mlsanity.com/paper/2505.15438/) (2025)
- [All You Need In Sign Language Production](https://mlsanity.com/paper/2201.01609/) (2022)
- [Signing Outside the Studio: Benchmarking Background Robustness for Continuous Sign Language Recognition](https://mlsanity.com/paper/2211.00448/) (2022)
- [A Hong Kong Sign Language Corpus Collected from Sign-interpreted TV News](https://mlsanity.com/paper/2405.00980/) (2024)
- [iSign: A Benchmark for Indian Sign Language Processing](https://mlsanity.com/paper/2407.05404/) (2024)

## Abstract (arXiv)

We propose a sign language translation system based on human keypoint estimation. It is well-known that many problems in the field of computer vision require a massive amount of dataset to train deep neural network models. The situation is even worse when it comes to the sign language translation problem as it is far more difficult to collect high-quality training data. In this paper, we introduce the KETI (short for Korea Electronics Technology Institute) sign language dataset which consists of 14,672 videos of high resolution and quality. Considering the fact that each country has a different and unique sign language, the KETI sign language dataset can be the starting line for further research on the Korean sign language translation. Using the KETI sign language dataset, we develop a neural network model for translating sign videos into natural language sentences by utilizing the human keypoints extracted from a face, hands, and body parts. The obtained human keypoint vector is normalized by the mean and standard deviation of the keypoints and used as input to our translation model based on the sequence-to-sequence architecture. As a result, we show that our approach is robust even when the size of the training data is not sufficient. Our translation model achieves 93.28% (55.28%, respectively) translation accuracy on the validation set (test set, respectively) for 105 sentences that can be used in emergency situations. We compare several types of our neural sign translation models based on different attention mechanisms in terms of classical metrics for measuring the translation performance.