# Enhancing Brazilian Sign Language Recognition through Skeleton Image Representation

arXiv: https://arxiv.org/abs/2404.19148 · HTML: https://mlsanity.com/paper/2404.19148/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [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)
- [Skeleton-DML: Deep Metric Learning for Skeleton-Based One-Shot Action Recognition](https://mlsanity.com/paper/2012.13823/) (2020)
- [Isolated Sign Language Recognition based on Tree Structure Skeleton Images](https://mlsanity.com/paper/2304.05403/) (2023)
- [StepNet: Spatial-temporal Part-aware Network for Isolated Sign Language Recognition](https://mlsanity.com/paper/2212.12857/) (2022)

## Built on by (most cited first, 3 of 3)

- [Proper Body Landmark Subset Enables More Accurate and 5X Faster Recognition of Isolated Signs in LIBRAS](https://mlsanity.com/paper/2510.24887/) (2025)
- [Dynamic LIBRAS Gesture Recognition via CNN over Spatiotemporal Matrix Representation](https://mlsanity.com/paper/2603.25863/) (2026)
- [The In-Car Sign Language Corpus (ICSL): A Multi-Modal Resource for Constrained-Space Sign Language Recognition](https://mlsanity.com/paper/2607.11341/) (2026)

## Abstract (arXiv)

Effective communication is paramount for the inclusion of deaf individuals in society. However, persistent communication barriers due to limited Sign Language (SL) knowledge hinder their full participation. In this context, Sign Language Recognition (SLR) systems have been developed to improve communication between signing and non-signing individuals. In particular, there is the problem of recognizing isolated signs (Isolated Sign Language Recognition, ISLR) of great relevance in the development of vision-based SL search engines, learning tools, and translation systems. This work proposes an ISLR approach where body, hands, and facial landmarks are extracted throughout time and encoded as 2-D images. These images are processed by a convolutional neural network, which maps the visual-temporal information into a sign label. Experimental results demonstrate that our method surpassed the state-of-the-art in terms of performance metrics on two widely recognized datasets in Brazilian Sign Language (LIBRAS), the primary focus of this study. In addition to being more accurate, our method is more time-efficient and easier to train due to its reliance on a simpler network architecture and solely RGB data as input.