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.

我们提出了一种通用的多语言文本检测框架，可以在场景图像中检测和识别已知和未知语言区域，不需要为未知语言收集有监督的训练数据或进行模型重新训练。