SWL-LSE: A Dataset of Health-Related Signs in Spanish Sign Language with an ISLR Baseline Method

Progress in automatic sign language recognition and translation has been hindered by the scarcity of datasets available for the training of machine learning algorithms, a challenge that is even more acute for languages with smaller signing communities, such as Spanish. In this paper, we introduce a...

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Bibliographic Details
Published in:Technologies (Basel) 2024-10, Vol.12 (10), p.205
Main Authors: Vázquez-Enríquez, Manuel, Alba-Castro, José Luis, Docío-Fernández, Laura, Rodríguez-Banga, Eduardo
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Applications programs
Artificial intelligence
Artificial neural networks
benchmark
Collaboration
Data collection
dataset
Datasets
Deafness
Deep learning
Dictionaries
dictionary
Identification and classification
Machine learning
Neural networks
Real time
Sign language
sign language recognition
spatial–temporal graph convolutional network
Technology application
Citations: Items that this one cites
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Summary:Progress in automatic sign language recognition and translation has been hindered by the scarcity of datasets available for the training of machine learning algorithms, a challenge that is even more acute for languages with smaller signing communities, such as Spanish. In this paper, we introduce a dataset of 300 isolated signs in Spanish Sign Language, collected online via a web application with contributions from 124 participants, resulting in a total of 8000 instances. This dataset, which is openly available, includes keypoints extracted using MediaPipe Holistic. The goal of this paper is to describe the construction and characteristics of the dataset and to provide a baseline classification method using a spatial–temporal graph convolutional network (ST-GCN) model, encouraging the scientific community to improve upon it. The experimental section offers a comparative analysis of the method’s performance on the new dataset, as well as on two other well-known datasets. The dataset, code, and web app used for data collection are freely available, and the web app can also be used to test classifier performance on-line in real-time.
ISSN:2227-7080
2227-7080
DOI:10.3390/technologies12100205