# Improving Handshape Representations for Sign Language Processing: A Graph Neural Network Approach

arXiv: https://arxiv.org/abs/2509.18309 · HTML: https://mlsanity.com/paper/2509.18309/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Builds on (most cited first, 8 of 8)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Skeleton Aware Multi-modal Sign Language Recognition](https://mlsanity.com/paper/2103.08833/) (2021)
- [Open-Domain Sign Language Translation Learned from Online Video](https://mlsanity.com/paper/2205.12870/) (2022)
- [Pose-based Sign Language Recognition using GCN and BERT](https://mlsanity.com/paper/2012.00781/) (2020)
- [American Sign Language Handshapes Reflect Pressures for Communicative Efficiency](https://mlsanity.com/paper/2406.04024/) (2024)
- [Exploring Strategies for Modeling Sign Language Phonology](https://mlsanity.com/paper/2310.00195/) (2023)

## Abstract (arXiv)

Handshapes serve a fundamental phonological role in signed languages, with American Sign Language employing approximately 50 distinct shapes. However,computational approaches rarely model handshapes explicitly, limiting both recognition accuracy and linguistic analysis.We introduce a novel graph neural network that separates temporal dynamics from static handshape configurations. Our approach combines anatomically-informed graph structures with contrastive learning to address key challenges in handshape recognition, including subtle interclass distinctions and temporal variations. We establish the first benchmark for structured handshape recognition in signing sequences, achieving 46% accuracy across 37 handshape classes (with baseline methods achieving 25%).