ANDRE, Maxime

Cleve, Anthony

Dumas, Bruno

Fink, Jerome

Frénay, Benoît

Meurant, Laurence

Meurice, Loup

Poitier, Pierre

English

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.</p

André, Maxime

Repository of the University of Namur

Sign Language-to-Text Dictionary with Lightweight Transformer Models

https://pure.unamur.be/ws/files/81113555/IJCAI_Sign_Language_1_.pdf

Sign Language to Text Dictionary with Lightweight Transformer Models

Sign Language to Text Dictionary with Lightweight Transformer Models

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Repository of the University of Namur