  Text detection is frequently used in vision-based mobile robots when they
need to interpret texts in their surroundings to perform a given task. For
instance, delivery robots in multilingual cities need to be capable of doing
multilingual text detection so that the robots can read traffic signs and road
markings. Moreover, the target languages change from region to region, implying
the need of efficiently re-training the models to recognize the novel/new
languages. However, collecting and labeling training data for novel languages
are cumbersome, and the efforts to re-train an existing/trained text detector
are considerable. Even worse, such a routine would repeat whenever a novel
language appears. This motivates us to propose a new problem setting for
tackling the aforementioned challenges in a more efficient way: "We ask for a
generalizable multilingual text detection framework to detect and identify both
seen and unseen language regions inside scene images without the requirement of
collecting supervised training data for unseen languages as well as model
re-training". To this end, we propose "MENTOR", the first work to realize a
learning strategy between zero-shot learning and few-shot learning for
multilingual scene text detection.
