  Writing assistance is an application closely related to human life and is
also a fundamental Natural Language Processing (NLP) research field. Its aim is
to improve the correctness and quality of input texts, with character checking
being crucial in detecting and correcting wrong characters. From the
perspective of the real world where handwriting occupies the vast majority,
characters that humans get wrong include faked characters (i.e., untrue
characters created due to writing errors) and misspelled characters (i.e., true
characters used incorrectly due to spelling errors). However, existing datasets
and related studies only focus on misspelled characters mainly caused by
phonological or visual confusion, thereby ignoring faked characters which are
more common and difficult. To break through this dilemma, we present
Visual-C$^3$, a human-annotated Visual Chinese Character Checking dataset with
faked and misspelled Chinese characters. To the best of our knowledge,
Visual-C$^3$ is the first real-world visual and the largest human-crafted
dataset for the Chinese character checking scenario. Additionally, we also
propose and evaluate novel baseline methods on Visual-C$^3$. Extensive
empirical results and analyses show that Visual-C$^3$ is high-quality yet
challenging. The Visual-C$^3$ dataset and the baseline methods will be publicly
available to facilitate further research in the community.
