A handwritten Chinese character image evaluation method based on CLIP pre-training alignment
Year: 2025
Type: conference-paper
Abstract: This paper proposed a method for evaluating handwritten Chinese character images using CLIP pre-training alignment. Traditional evaluation methods face challenges due to the lack of a unified standard and the need to consider both structure and style. Therefore, we introduced a CLIP model that aligns images with text descriptions through contrastive learning and designed an SSF-Attn module to enhance Chinese Handwritten Hanzi style perception. Additionally, we constructed a multimodal dataset and proposed the SSHID metric to measure style differences. Finally, the experiments shown that our HS-CLIP network outperforms traditional methods in image-text alignment, and SSHID outperforms FID in Chinese Handwritten Hanzi style perception. Future work will focus on improving the model's interpretability of Chinese Handwritten Hanzi image features.
DOI: 10.1117/12.3076714
Institution: Northwest Normal University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4414263286
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