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arXiv · 2601.11248

Language-Agnostic Visual Embeddings for Cross-Script Handwriting Retrieval

Fangke Chen·Tianhao Dong·Sirry Chen·Guobin Zhang·Yishu Zhang·Yining Chen
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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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Fangke Chen, Tianhao Dong, Sirry Chen, Guobin Zhang, Yishu Zhang, Yining Chen. 2026-01-16. Language-Agnostic Visual Embeddings for Cross-Script Handwriting Retrieval. https://arxiv.org/abs/2601.11248

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