# Skeleton-based sign language recognition using a dual-stream spatio-temporal dynamic graph convolutional network

arXiv: https://arxiv.org/abs/2509.08661 · HTML: https://mlsanity.com/paper/2509.08661/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 16 references that are in the map.
- First posted on arXiv 10 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [Skeleton Aware Multi-modal Sign Language Recognition](https://mlsanity.com/paper/2103.08833/) (2021)
- [Uni-Sign: Toward Unified Sign Language Understanding at Scale](https://mlsanity.com/paper/2501.15187/) (2025)
- [Natural Language-Assisted Sign Language Recognition](https://mlsanity.com/paper/2303.12080/) (2023)
- [A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU](https://mlsanity.com/paper/2305.17473/) (2023)
- [Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition](https://mlsanity.com/paper/1901.11164/) (2019)
- [Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble](https://mlsanity.com/paper/2110.06161/) (2021)
- [Hierarchical Windowed Graph Attention Network and a Large Scale Dataset for Isolated Indian Sign Language Recognition](https://mlsanity.com/paper/2407.14224/) (2024)
- [Dynamic Spatial-Temporal Aggregation for Skeleton-Aware Sign Language Recognition](https://mlsanity.com/paper/2403.12519/) (2024)
- [Siformer: Feature-isolated Transformer for Efficient Skeleton-based Sign Language Recognition](https://mlsanity.com/paper/2503.20436/) (2025)
- [Combining Efficient and Precise Sign Language Recognition: Good pose estimation library is all you need](https://mlsanity.com/paper/2210.00893/) (2022)
- [SignBart -- New approach with the skeleton sequence for Isolated Sign language Recognition](https://mlsanity.com/paper/2506.21592/) (2025)

## Abstract (arXiv)

Isolated Sign Language Recognition (ISLR) is challenged by gestures that are morphologically similar yet semantically distinct, a problem rooted in the complex interplay between hand shape and motion trajectory. Existing methods, often relying on a single reference frame, struggle to resolve this geometric ambiguity. This paper introduces Dual-SignLanguageNet (DSLNet), a dual-reference, dual-stream architecture that decouples and models gesture morphology and trajectory in separate, complementary coordinate systems. The architecture processes these streams through specialized networks: a topology-aware graph convolution models the view-invariant shape from a wrist-centric frame, while a Finsler geometry-based encoder captures the context-aware trajectory from a facial-centric frame. These features are then integrated via a geometry-driven optimal transport fusion mechanism. DSLNet sets a new state-of-the-art, achieving 93.70%, 89.97%, and 99.79% accuracy on the challenging WLASL-100, WLASL-300, and LSA64 datasets, respectively, with significantly fewer parameters than competing models.