# LSA64: An Argentinian Sign Language Dataset

arXiv: https://arxiv.org/abs/2310.17429 · HTML: https://mlsanity.com/paper/2310.17429/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 9 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Built on by (most cited first, 15 of 15)

- [A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities](https://mlsanity.com/paper/2408.05436/) (2024)
- [MM-WLAuslan: Multi-View Multi-Modal Word-Level Australian Sign Language Recognition Dataset](https://mlsanity.com/paper/2410.19488/) (2024)
- [Isharah: A Large-Scale Multi-Scene Dataset for Continuous Sign Language Recognition](https://mlsanity.com/paper/2506.03615/) (2025)
- [BAUST Lipi: A BdSL Dataset with Deep Learning Based Bangla Sign Language Recognition](https://mlsanity.com/paper/2408.10518/) (2024)
- [Logos as a Well-Tempered Pre-train for Sign Language Recognition](https://mlsanity.com/paper/2505.10481/) (2025)
- [Training Strategies for Isolated Sign Language Recognition](https://mlsanity.com/paper/2412.11553/) (2024)
- [Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks](https://mlsanity.com/paper/2506.04367/) (2025)
- [DexAvatar: 3D Sign Language Reconstruction with Hand and Body Pose Priors](https://mlsanity.com/paper/2512.21054/) (2025)
- [ISLR101: an Iranian Word-Level Sign Language Recognition Dataset](https://mlsanity.com/paper/2503.12451/) (2025)
- [The TUB Sign Language Corpus Collection](https://mlsanity.com/paper/2508.05374/) (2025)
- [SSL-SLR: Self-Supervised Representation Learning for Sign Language Recognition](https://mlsanity.com/paper/2509.05188/) (2025)
- [Reconnaissance Automatique des Langues des Signes : Une Approche Hybrid'ee CNN-LSTM Bas'ee sur Mediapipe](https://mlsanity.com/paper/2510.22011/) (2025)
- [Sign-Language Datasets at Scale: A Comprehensive Survey on Resources, Benchmarks, and Annotation Standards](https://mlsanity.com/paper/2606.19352/) (2026)
- [A HamNoSys-Guided Dataset and Baselines for Fine-Grained Isolated Handshape Recognition in Sign Language](https://mlsanity.com/paper/2608.10588/) (2026)
- [JSL-DC: A Word-Level Japanese Sign Language Dataset with Linguist-Derived Descriptions for Distinguishing Confusable Signs](https://mlsanity.com/paper/2608.18412/) (2026)

## Abstract (arXiv)

Automatic sign language recognition is a research area that encompasses human-computer interaction, computer vision and machine learning. Robust automatic recognition of sign language could assist in the translation process and the integration of hearing-impaired people, as well as the teaching of sign language to the hearing population. Sign languages differ significantly in different countries and even regions, and their syntax and semantics are different as well from those of written languages. While the techniques for automatic sign language recognition are mostly the same for different languages, training a recognition system for a new language requires having an entire dataset for that language. This paper presents a dataset of 64 signs from the Argentinian Sign Language (LSA). The dataset, called LSA64, contains 3200 videos of 64 different LSA signs recorded by 10 subjects, and is a first step towards building a comprehensive research-level dataset of Argentinian signs, specifically tailored to sign language recognition or other machine learning tasks. The subjects that performed the signs wore colored gloves to ease the hand tracking and segmentation steps, allowing experiments on the dataset to focus specifically on the recognition of signs. We also present a pre-processed version of the dataset, from which we computed statistics of movement, position and handshape of the signs.