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

arXiv: https://arxiv.org/abs/2311.11268 · HTML: https://mlsanity.com/paper/2311.11268/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 55 references that are in the map.
- First posted on arXiv 19 Nov 2023.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Scene Text Detection and Recognition: The Deep Learning Era](https://mlsanity.com/paper/1811.04256/) (2018)
- [PP-OCRv3: More Attempts for the Improvement of Ultra Lightweight OCR System](https://mlsanity.com/paper/2206.03001/) (2022)
- [Are we ready for a new paradigm shift? A Survey on Visual Deep MLP](https://mlsanity.com/paper/2111.04060/) (2021)
- [The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking](https://mlsanity.com/paper/2203.00991/) (2022)
- [On the (In)Effectiveness of Large Language Models for Chinese Text Correction](https://mlsanity.com/paper/2307.09007/) (2023)
- [Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking](https://mlsanity.com/paper/2105.12306/) (2021)
- [Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revision](https://mlsanity.com/paper/2204.03685/) (2022)
- [Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking](https://mlsanity.com/paper/2210.10320/) (2022)
- [Contrastive Learning with Hard Negative Entities for Entity Set Expansion](https://mlsanity.com/paper/2204.07789/) (2022)
- [General and Domain Adaptive Chinese Spelling Check with Error Consistent Pretraining](https://mlsanity.com/paper/2203.10929/) (2022)
- [Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction](https://mlsanity.com/paper/2210.10442/) (2022)
- [Automatic Context Pattern Generation for Entity Set Expansion](https://mlsanity.com/paper/2207.08087/) (2022)
- [Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment](https://mlsanity.com/paper/2307.16210/) (2023)
- [Vision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment](https://mlsanity.com/paper/2302.08774/) (2023)
- [Rethinking Masked Language Modeling for Chinese Spelling Correction](https://mlsanity.com/paper/2305.17721/) (2023)
- [A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check](https://mlsanity.com/paper/2310.09119/) (2023)
- [Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking](https://mlsanity.com/paper/2207.09217/) (2022)
- [MESED: A Multi-modal Entity Set Expansion Dataset with Fine-grained Semantic Classes and Hard Negative Entities](https://mlsanity.com/paper/2307.14878/) (2023)
- [Text revision in Scientific Writing Assistance: An Overview](https://mlsanity.com/paper/2303.16726/) (2023)

## Built on by (most cited first, 22 of 22)

- [RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback](https://mlsanity.com/paper/2312.00849/) (2023)
- [TableBench: A Comprehensive and Complex Benchmark for Table Question Answering](https://mlsanity.com/paper/2408.09174/) (2024)
- [Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey](https://mlsanity.com/paper/2411.17558/) (2024)
- [Table-R1: Region-based Reinforcement Learning for Table Understanding](https://mlsanity.com/paper/2505.12415/) (2025)
- [When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Models](https://mlsanity.com/paper/2402.11100/) (2024)
- [DAST: Context-Aware Compression in LLMs via Dynamic Allocation of Soft Tokens](https://mlsanity.com/paper/2502.11493/) (2025)
- [Refine Knowledge of Large Language Models via Adaptive Contrastive Learning](https://mlsanity.com/paper/2502.07184/) (2025)
- [One Example Shown, Many Concepts Known! Counterexample-Driven Conceptual Reasoning in Mathematical LLMs](https://mlsanity.com/paper/2502.10454/) (2025)
- [Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction](https://mlsanity.com/paper/2402.11420/) (2024)
- [Cognitive Mismatch in Multimodal Large Language Models for Discrete Symbol Understanding](https://mlsanity.com/paper/2603.18472/) (2026)
- [TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning](https://mlsanity.com/paper/2510.06217/) (2025)
- [Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Framework](https://mlsanity.com/paper/2402.11422/) (2024)
- [Raw Text is All you Need: Knowledge-intensive Multi-turn Instruction Tuning for Large Language Model](https://mlsanity.com/paper/2407.03040/) (2024)
- [Exploring the Implicit Semantic Ability of Multimodal Large Language Models: A Pilot Study on Entity Set Expansion](https://mlsanity.com/paper/2501.00330/) (2024)
- [Loss-Aware Curriculum Learning for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2501.00334/) (2024)
- [Multilingual Multimodal Software Developer for Code Generation](https://mlsanity.com/paper/2507.08719/) (2025)
- [Deep Thought Alignment: Trajectory-Level Latent Distillation for Video Reasoning](https://mlsanity.com/paper/2608.16316/) (2026)
- [Corrections Meet Explanations: A Unified Framework for Explainable Grammatical Error Correction](https://mlsanity.com/paper/2502.15261/) (2025)
- [Vision Language Models Are Not (Yet) Spelling Correctors](https://mlsanity.com/paper/2509.17418/) (2025)
- [CL2GEC: A Multi-Discipline Benchmark for Continual Learning in Chinese Literature Grammatical Error Correction](https://mlsanity.com/paper/2509.13672/) (2025)
- [HCSU: A Dataset and Benchmark for Fine-Grained Historical Calligraphy Style Understanding](https://mlsanity.com/paper/2607.04147/) (2026)
- [TextVerifier: Seeing Beyond Recognition to Verify Chinese Text Rendering](https://mlsanity.com/paper/2610.19551/) (2026)

## Abstract (arXiv)

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-C3, a human-annotated Visual Chinese Character Checking dataset with faked and misspelled Chinese characters. To the best of our knowledge, Visual-C3 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-C3. Extensive empirical results and analyses show that Visual-C3 is high-quality yet challenging. The Visual-C3 dataset and the baseline methods will be publicly available to facilitate further research in the community.