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.
@article{arxiv.2603.26768,
title = {Aesthetic Assessment of Chinese Handwritings Based on Vision Language Models},
author = {Chen Zheng and Yuxuan Lai and Haoyang Lu and Wentao Ma and Jitao Yang and Jian Wang},
journal= {arXiv preprint arXiv:2603.26768},
year = {2026}
}