  Chinese character recognition has attracted much research interest due to its
wide applications. Although it has been studied for many years, some issues in
this field have not been completely resolved yet, e.g. the zero-shot problem.
Previous character-based and radical-based methods have not fundamentally
addressed the zero-shot problem since some characters or radicals in test sets
may not appear in training sets under a data-hungry condition. Inspired by the
fact that humans can generalize to know how to write characters unseen before
if they have learned stroke orders of some characters, we propose a
stroke-based method by decomposing each character into a sequence of strokes,
which are the most basic units of Chinese characters. However, we observe that
there is a one-to-many relationship between stroke sequences and Chinese
characters. To tackle this challenge, we employ a matching-based strategy to
transform the predicted stroke sequence to a specific character. We evaluate
the proposed method on handwritten characters, printed artistic characters, and
scene characters. The experimental results validate that the proposed method
outperforms existing methods on both character zero-shot and radical zero-shot
tasks. Moreover, the proposed method can be easily generalized to other
languages whose characters can be decomposed into strokes.
