Language-Agnostic Visual Embeddings for Cross-Script Handwriting Retrieval

• URL: http://arxiv.org/abs/2601.11248v1
• Date: Fri, 16 Jan 2026 12:55:41 GMT
• Title: Language-Agnostic Visual Embeddings for Cross-Script Handwriting Retrieval
• Authors: Fangke Chen, Tianhao Dong, Sirry Chen, Guobin Zhang, Yishu Zhang, Yining Chen,
• Abstract summary: We propose a lightweight asymmetric dual-encoder framework that learns unified, style-invariant visual embeddings.<n>By jointly optimizing instance-level alignment and class-level semantic consistency, our approach anchors visual embeddings to language-agnostic semantic prototypes.<n> Experiments show that our method outperforms 28 baselines and state-of-the-art accuracy on within-language retrieval benchmarks.
• Score: 5.359439761925416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Handwritten word retrieval is vital for digital archives but remains challenging due to large handwriting variability and cross-lingual semantic gaps. While large vision-language models offer potential solutions, their prohibitive computational costs hinder practical edge deployment. To address this, we propose a lightweight asymmetric dual-encoder framework that learns unified, style-invariant visual embeddings. By jointly optimizing instance-level alignment and class-level semantic consistency, our approach anchors visual embeddings to language-agnostic semantic prototypes, enforcing invariance across scripts and writing styles. Experiments show that our method outperforms 28 baselines and achieves state-of-the-art accuracy on within-language retrieval benchmarks. We further conduct explicit cross-lingual retrieval, where the query language differs from the target language, to validate the effectiveness of the learned cross-lingual representations. Achieving strong performance with only a fraction of the parameters required by existing models, our framework enables accurate and resource-efficient cross-script handwriting retrieval.

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