Zero-Shot Chinese Character Recognition with Hierarchical Multi-Granularity Image-Text Aligning

• URL: http://arxiv.org/abs/2505.24837v1
• Date: Fri, 30 May 2025 17:39:14 GMT
• Title: Zero-Shot Chinese Character Recognition with Hierarchical Multi-Granularity Image-Text Aligning
• Authors: Yinglian Zhu, Haiyang Yu, Qizao Wang, Wei Lu, Xiangyang Xue, Bin Li,
• Abstract summary: Chinese characters exhibit unique structures and compositional rules, allowing for the use of fine-grained semantic information in representation.<n>We propose a Hierarchical Multi-Granularity Image-Text Aligning (Hi-GITA) framework based on a contrastive paradigm.<n>Our proposed Hi-GITA outperforms existing zero-shot CCR methods.
• Score: 52.92837273570818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chinese Character Recognition (CCR) is a fundamental technology for intelligent document processing. Unlike Latin characters, Chinese characters exhibit unique spatial structures and compositional rules, allowing for the use of fine-grained semantic information in representation. However, existing approaches are usually based on auto-regressive as well as edit distance post-process and typically rely on a single-level character representation. In this paper, we propose a Hierarchical Multi-Granularity Image-Text Aligning (Hi-GITA) framework based on a contrastive paradigm. To leverage the abundant fine-grained semantic information of Chinese characters, we propose multi-granularity encoders on both image and text sides. Specifically, the Image Multi-Granularity Encoder extracts hierarchical image representations from character images, capturing semantic cues from localized strokes to holistic structures. The Text Multi-Granularity Encoder extracts stroke and radical sequence representations at different levels of granularity. To better capture the relationships between strokes and radicals, we introduce Multi-Granularity Fusion Modules on the image and text sides, respectively. Furthermore, to effectively bridge the two modalities, we further introduce a Fine-Grained Decoupled Image-Text Contrastive loss, which aligns image and text representations across multiple granularities. Extensive experiments demonstrate that our proposed Hi-GITA significantly outperforms existing zero-shot CCR methods. For instance, it brings about 20% accuracy improvement in handwritten character and radical zero-shot settings. Code and models will be released soon.

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