LSA64: An Argentinian Sign Language Dataset

TL;DR

This paper introduces LSA64, a new Argentinian Sign Language dataset with 3200 videos of 64 signs, aimed at advancing automatic recognition and machine learning research for Argentinian Sign Language.

Contribution

The paper presents the first comprehensive dataset of Argentinian Sign Language signs, including pre-processed data and statistical analysis to facilitate recognition research.

Findings

Dataset contains 3200 videos of 64 signs

Subjects wore colored gloves for easier hand tracking

Pre-processed data includes movement, position, and handshape statistics

Abstract

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.