Cross-Lingual Learning in Multilingual Scene Text Recognition
Jeonghun Baek Yusuke Matsui Kiyoharu Aizawa
In this paper, we investigate cross-lingual learning (CLL) for multilingual
scene text recognition (STR). CLL transfers knowledge from one language to
another. We aim to find the condition that exploits knowledge from
high-resource languages for improving performance in low-resource languages. To
do so, we first examine if two general insights about CLL discussed in previous
works are applied to multilingual STR: (1) Joint learning with high- and
low-resource languages may reduce performance on low-resource languages, and
(2) CLL works best between typologically similar languages. Through extensive
experiments, we show that two general insights may not be applied to
multilingual STR. After that, we show that the crucial condition for CLL is the
dataset size of high-resource languages regardless of the kind of high-resource
languages. Our code, data, and models are available at
https://github.com/ku21fan/CLL-STR.