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.
@article{arxiv.2603.03316,
title = {The Influence of Iconicity in Transfer Learning for Sign Language Recognition},
author = {Keren Artiaga and Conor Lynch and Haithem Afli and Mohammed Hasanuzzaman},
journal= {arXiv preprint arXiv:2603.03316},
year = {2026}
}