  There are ubiquitous distribution shifts in the real world. However, deep
neural networks (DNNs) are easily biased towards the training set, which causes
severe performance degradation when they receive out-of-distribution data. Many
methods are studied to train models that generalize under various distribution
shifts in the literature of domain generalization (DG). However, the recent
DomainBed and WILDS benchmarks challenged the effectiveness of these methods.
Aiming at the problems in the existing research, we propose a new domain
generalization task for handwritten Chinese character recognition (HCCR) to
enrich the application scenarios of DG method research. We evaluate eighteen DG
methods on the proposed PaHCC (Printed and Handwritten Chinese Characters)
dataset and show that the performance of existing methods on this dataset is
still unsatisfactory. Besides, under a designed dynamic DG setting, we reveal
more properties of DG methods and argue that only the leave-one-domain-out
protocol is unreliable. We advocate that researchers in the DG community refer
to dynamic performance of methods for more comprehensive and reliable
evaluation. Our dataset and evaluations bring new perspectives to the community
for more substantial progress. We will make our dataset public with the article
published to facilitate the study of domain generalization.
