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

Instance Segmentation for Chinese Character Stroke Extraction, Datasets and Benchmarks