Abstract
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.
Abstract (translated)
汉字书写是学习汉语的基础。以往自动化评估方法常将评分定义为回归问题，但仅提供分数的反馈缺乏可操作的指导，限制了其在提升书写技能方面的效果。本文利用视觉-语言模型（VLMs）分析手写汉字质量并生成多层级反馈。具体而言，我们研究了两种反馈生成任务：简单等级反馈（任务1）和丰富的描述性反馈（任务2）。我们探索了基于低秩适应（LoRA）的微调策略与上下文学习方法，以将审美评估知识融入VLMs。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项评测赛道中达到最先进水平。
URL
https://arxiv.org/abs/2603.26768
