  Stroke is the basic element of Chinese character and stroke extraction has
been an important and long-standing endeavor. Existing stroke extraction
methods are often handcrafted and highly depend on domain expertise due to the
limited training data. Moreover, there are no standardized benchmarks to
provide a fair comparison between different stroke extraction methods, which,
we believe, is a major impediment to the development of Chinese character
stroke understanding and related tasks. In this work, we present the first
public available Chinese Character Stroke Extraction (CCSE) benchmark, with two
new large-scale datasets: Kaiti CCSE (CCSE-Kai) and Handwritten CCSE (CCSE-HW).
With the large-scale datasets, we hope to leverage the representation power of
deep models such as CNNs to solve the stroke extraction task, which, however,
remains an open question. To this end, we turn the stroke extraction problem
into a stroke instance segmentation problem. Using the proposed datasets to
train a stroke instance segmentation model, we surpass previous methods by a
large margin. Moreover, the models trained with the proposed datasets benefit
the downstream font generation and handwritten aesthetic assessment tasks. We
hope these benchmark results can facilitate further research. The source code
and datasets are publicly available at: https://github.com/lizhaoliu-Lec/CCSE.
