LadderMoE: Ladder-Side Mixture of Experts Adapters for Bronze Inscription Recognition

Rixin Zhou; Peiqiang Qiu; Qian Zhang; Chuntao Li; Xi Yang

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arXiv · 2510.01651

LadderMoE: Ladder-Side Mixture of Experts Adapters for Bronze Inscription Recognition

Rixin Zhou·Peiqiang Qiu·Qian Zhang·Chuntao Li·Xi Yang

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Abstract

Bronze inscriptions (BI), engraved on ritual vessels, constitute a crucial stage of early Chinese writing and provide indispensable evidence for archaeological and historical studies. However, automatic BI recognition remains difficult due to severe visual degradation, multi-domain variability across photographs, rubbings, and tracings, and an extremely long-tailed character distribution. To address these challenges, we curate a large-scale BI dataset comprising 22454 full-page images and 198598 annotated characters spanning 6658 unique categories, enabling robust cross-domain evaluation. Building on this resource, we develop a two-stage detection-recognition pipeline that first localizes inscriptions and then transcribes individual characters. To handle heterogeneous domains and rare classes, we equip the pipeline with LadderMoE, which augments a pretrained CLIP encoder with ladder-style MoE adapters, enabling dynamic expert specialization and stronger robustness. Comprehensive experiments on single-character and full-page recognition tasks demonstrate that our method substantially outperforms state-of-the-art scene text recognition baselines, achieving superior accuracy across head, mid, and tail categories as well as all acquisition modalities. These results establish a strong foundation for bronze inscription recognition and downstream archaeological analysis.

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BibTeX RIS

Rixin Zhou, Peiqiang Qiu, Qian Zhang, Chuntao Li, Xi Yang. 2025-10-02. LadderMoE: Ladder-Side Mixture of Experts Adapters for Bronze Inscription Recognition. https://arxiv.org/abs/2510.01651

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