用视觉语言模型分析汉字手写美感，给出可改进的详细反馈。
Aesthetic Assessment of Chinese Handwritings Based on Vision Language Models
- 用视觉语言模型分析手写汉字质量，生成多层级反馈。
- 在CCL 2025评测中表现领先，支持评分与描述性反馈两种任务。
- 适合需要个性化手写指导的学习者和教育应用开发者。
汉字手写是学习中文的基础。以往自动化评估常将评分视为回归问题，仅提供分数而缺乏可操作建议，难以有效帮助学习者提升。本文利用视觉语言模型（VLMs）分析手写汉字质量，并生成多层次反馈。具体研究两类任务：简单评分反馈（任务1）与丰富描述性反馈（任务2）。我们探索了基于低秩适配（LoRA）的微调策略及上下文学习方法，将审美评估知识融入VLM。实验结果表明，该方法在CCL 2025手写汉字质量评估工作坊的多个评测赛道上达到当前最优性能。
原文摘要 · Abstract (English)
The handwriting of Chinese characters is a fundamental aspect of learning the Chinese language. Previous automated assessment methods often framed scoring as a regression problem. However, this score-only feedback lacks actionable guidance, which limits its effectiveness in helping learners improve their handwriting skills. In this paper, we leverage vision-language models (VLMs) to analyze the quality of handwritten Chinese characters and generate multi-level feedback. Specifically, we investigate two feedback generation tasks: simple grade feedback (Task 1) and enriched, descriptive feedback (Task 2). We explore both low-rank adaptation (LoRA)-based fine-tuning strategies and in-context learning methods to integrate aesthetic assessment knowledge into VLMs. Experimental results show that our approach achieves state-of-the-art performances across multiple evaluation tracks in the CCL 2025 workshop on evaluation of handwritten Chinese character quality.
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