# The NGT200 Dataset: Geometric Multi-View Isolated Sign Recognition

arXiv: https://arxiv.org/abs/2409.15284 · HTML: https://mlsanity.com/paper/2409.15284/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 3 Sep 2024.

## Builds on (most cited first, 19 of 19)

- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language](https://mlsanity.com/paper/2008.08143/) (2020)
- [MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language](https://mlsanity.com/paper/1812.01053/) (2018)
- [Skeleton Aware Multi-modal Sign Language Recognition](https://mlsanity.com/paper/2103.08833/) (2021)
- [Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective](https://mlsanity.com/paper/1908.08597/) (2019)
- [AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methods](https://mlsanity.com/paper/2008.00932/) (2020)
- [Including Signed Languages in Natural Language Processing](https://mlsanity.com/paper/2105.05222/) (2021)
- [Quantitative Survey of the State of the Art in Sign Language Recognition](https://mlsanity.com/paper/2008.09918/) (2020)
- [Fast, Expressive SE(n) Equivariant Networks through Weight-Sharing in Position-Orientation Space](https://mlsanity.com/paper/2310.02970/) (2023)
- [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)
- [Sign Language Production: A Review](https://mlsanity.com/paper/2103.15910/) (2021)
- [Evaluating the Immediate Applicability of Pose Estimation for Sign Language Recognition](https://mlsanity.com/paper/2104.10166/) (2021)
- [Systemic Biases in Sign Language AI Research: A Deaf-Led Call to Reevaluate Research Agendas](https://mlsanity.com/paper/2403.02563/) (2024)
- [Grounding Continuous Representations in Geometry: Equivariant Neural Fields](https://mlsanity.com/paper/2406.05753/) (2024)
- [The Sem-Lex Benchmark: Modeling ASL Signs and Their Phonemes](https://mlsanity.com/paper/2310.00196/) (2023)
- [Skeletal Graph Self-Attention: Embedding a Skeleton Inductive Bias into Sign Language Production](https://mlsanity.com/paper/2112.05277/) (2021)
- [Exploring Strategies for Modeling Sign Language Phonology](https://mlsanity.com/paper/2310.00195/) (2023)

## Built on by (most cited first, 2 of 2)

- [What's the Point? Spatial Grammar & Index Resolution for Sign Language Processing](https://mlsanity.com/paper/2606.08056/) (2026)
- [Beyond BLEU: A Case for Redefining Sign Language Translation Benchmarks](https://mlsanity.com/paper/2609.03734/) (2026)

## Abstract (arXiv)

Sign Language Processing (SLP) provides a foundation for a more inclusive future in language technology; however, the field faces several significant challenges that must be addressed to achieve practical, real-world applications. This work addresses multi-view isolated sign recognition (MV-ISR), and highlights the essential role of 3D awareness and geometry in SLP systems. We introduce the NGT200 dataset, a novel spatio-temporal multi-view benchmark, establishing MV-ISR as distinct from single-view ISR (SV-ISR). We demonstrate the benefits of synthetic data and propose conditioning sign representations on spatial symmetries inherent in sign language. Leveraging an SE(2) equivariant model improves MV-ISR performance by 8%-22% over the baseline.