Sign Language-to-Text Dictionary with Lightweight Transformer Models

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Résumé

The recent advances in deep learning have been beneficial to automatic sign language recognition (SLR). However, free-to-access, usable, and accessible tools are still not widely available to the deaf community. The need for a sign language-to-text dictionary was raised by a bilingual deaf school in Belgium and linguist experts in sign languages (SL) in order to improve the autonomy of students. To meet that need, an efficient SLR system was built based on a specific transformer model. The proposed system is able to recognize 700 different signs, with a top-10 accuracy of 83%. Those results are competitive with other systems in the literature while using 10 times less parameters than existing solutions. The integration of this model into a usable and accessible web application for the dictionary is also introduced. A user-centered human-computer interaction (HCI) methodology was followed to design and implement the user interface. To the best of our knowledge, this is the first publicly released sign language-to-text dictionary using video captured by a standard camera.

langue originaleAnglais
titreProceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
Sous-titreAI for Social Good track
rédacteurs en chefEdith Elkind
EditeurInternational Joint Conferences on Artificial Intelligence
Pages5968-5976
Nombre de pages9
ISBN (Electronique)9781956792034
Etat de la publicationPublié - 2023
Evénement32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 - Macao, Chine
Durée: 19 août 202325 août 2023

Série de publications

NomIJCAI International Joint Conference on Artificial Intelligence
Volume2023-August
ISSN (imprimé)1045-0823

Une conférence

Une conférence32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
Pays/TerritoireChine
La villeMacao
période19/08/2325/08/23

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