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.
Abstract (translated)
青铜铭文（BI）刻在祭祀器皿上，构成了早期中国文字发展的一个重要阶段，并为考古学和历史研究提供了不可或缺的证据。然而，由于视觉退化严重、不同照片、拓片和描摹中的多领域变化以及极其长尾的文字分布，自动识别BI仍然具有挑战性。为了应对这些挑战，我们整理了一个大规模的数据集，包含22454张全页图像和198598个注释字符，涵盖了6658种独特的类别，从而能够进行稳健的跨领域评估。在此基础上，我们开发了一条两阶段检测-识别流水线，首先定位铭文，然后转录单个文字。为了处理异质领域和稀有类别的问题，我们在该流水线上配备了LadderMoE，这是一种通过添加类似梯度式的专家适配器来增强预训练的CLIP编码器的方法，从而实现动态专家专业化并提高更强的鲁棒性。在单一字符和全页识别任务上的全面实验表明，我们的方法显著优于现有的场景文本识别基线，在头部、中段和尾部类别以及所有获取模式下都取得了更高的准确性。这些结果为青铜铭文识别及下游考古分析奠定了坚实的基础。
URL
https://arxiv.org/abs/2510.01651
