  Open-set text recognition, which aims to address both novel characters and
previously seen ones, is one of the rising subtopics in the text recognition
field. However, the current open-set text recognition solutions only focuses on
horizontal text, which fail to model the real-life challenges posed by the
variety of writing directions in real-world scene text. Multi-orientation text
recognition, in general, faces challenges from the diverse image aspect ratios,
significant imbalance in data amount, and domain gaps between orientations. In
this work, we first propose a Multi-Oriented Open-Set Text Recognition task
(MOOSTR) to model the challenges of both novel characters and writing direction
variety. We then propose a Multi-Orientation Sharing Experts (MOoSE) framework
as a strong baseline solution. MOoSE uses a mixture-of-experts scheme to
alleviate the domain gaps between orientations, while exploiting common
structural knowledge among experts to alleviate the data scarcity that some
experts face. The proposed MOoSE framework is validated by ablative
experiments, and also tested for feasibility on the existing open-set
benchmark. Code, models, and documents are available at:
https://github.com/lancercat/Moose/
