# Isolated Sign Language Recognition with Segmentation and Pose Estimation

arXiv: https://arxiv.org/abs/2512.14876 · HTML: https://mlsanity.com/paper/2512.14876/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 16 Dec 2025.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [Skeleton Aware Multi-modal Sign Language Recognition](https://mlsanity.com/paper/2103.08833/) (2021)
- [Natural Language-Assisted Sign Language Recognition](https://mlsanity.com/paper/2303.12080/) (2023)
- [Voxtral](https://mlsanity.com/paper/2507.13264/) (2025)
- [ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition](https://mlsanity.com/paper/2304.05934/) (2023)
- [BlazePose GHUM Holistic: Real-time 3D Human Landmarks and Pose Estimation](https://mlsanity.com/paper/2206.11678/) (2022)
- [Self-Supervised Video Transformers for Isolated Sign Language Recognition](https://mlsanity.com/paper/2309.02450/) (2023)

## Abstract (arXiv)

The recent surge in large language models has automated translations of spoken and written languages. However, these advances remain largely inaccessible to American Sign Language (ASL) users, whose language relies on complex visual cues. Isolated sign language recognition (ISLR) - the task of classifying videos of individual signs - can help bridge this gap but is currently limited by scarce per-sign data, high signer variability, and substantial computational costs. We propose a model for ISLR that reduces computational requirements while maintaining robustness to signer variation. Our approach integrates (i) a pose estimation pipeline to extract hand and face joint coordinates, (ii) a segmentation module that isolates relevant information, and (iii) a ResNet-Transformer backbone to jointly model spatial and temporal dependencies.