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Writing assistance is an application closely related to human life and isalso a fundamental Natural Language Processing (NLP) research field. Its aim isto improve the correctness and quality of input texts, with character checkingbeing crucial in detecting and correcting wrong characters. From theperspective of the real world where handwriting occupies the vast majority,characters that humans get wrong include faked characters (i.e., untruecharacters created due to writing errors) and misspelled characters (i.e., truecharacters used incorrectly due to spelling errors). However, existing datasetsand related studies only focus on misspelled characters mainly caused byphonological or visual confusion, thereby ignoring faked characters which aremore common and difficult. To break through this dilemma, we presentVisual-C$^3$, a human-annotated Visual Chinese Character Checking dataset withfaked and misspelled Chinese characters. To the best of our knowledge,Visual-C$^3$ is the first real-world visual and the largest human-crafteddataset for the Chinese character checking scenario. Additionally, we alsopropose and evaluate novel baseline methods on Visual-C$^3$. Extensiveempirical results and analyses show that Visual-C$^3$ is high-quality yetchallenging. The Visual-C$^3$ dataset and the baseline methods will be publiclyavailable to facilitate further research in the community.

Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters