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arXiv · 2603.03316

The Influence of Iconicity in Transfer Learning for Sign Language Recognition

Keren Artiaga·Conor Lynch·Haithem Afli·Mohammed Hasanuzzaman
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Abstract

Most sign language recognition research relies on Transfer Learning (TL) from vision-based datasets such as ImageNet. Some extend this to alternatively available language datasets, often focusing on signs with cross-linguistic similarities. This body of work examines the necessity of these likenesses on effective knowledge transfer by comparing TL performance between iconic signs of two different sign language pairs: Chinese to Arabic and Greek to Flemish. Google Mediapipe was utilised as an input feature extractor, enabling spatial information of these signs to be processed with a Multilayer Perceptron architecture and the temporal information with a Gated Recurrent Unit. Experimental results showed a 7.02% improvement for Arabic and 1.07% for Flemish when conducting iconic TL from Chinese and Greek respectively.

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Keren Artiaga, Conor Lynch, Haithem Afli, Mohammed Hasanuzzaman. 2026-02-09. The Influence of Iconicity in Transfer Learning for Sign Language Recognition. https://doi.org/10.1007/978-3-031-70239-6_16

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