用跨字体图像检索破解未释读甲骨文
A Cross-Font Image Retrieval Network for Recognizing Undeciphered Oracle Bone Inscriptions
- 构建孪生网络，融合多尺度特征提取结构线索
- 在三个数据集上实现未释读甲骨文的精准匹配
- 适合古文字研究与计算机辅助考释场景
甲骨文是中国最早成熟的文字系统，标志着象形文字发展的重要阶段。然而，大量未释读的甲骨文字仍给学者带来巨大挑战，传统古文字研究方法耗时耗力。本文提出跨字体图像检索网络（CFIRN），通过建立甲骨文与其他文字形式的关联，模拟古文字学家的解读行为来破译甲骨文。具体地，网络采用孪生架构，提取不同字体字符图像的深层特征，利用多尺度特征融合（MFI）模块和多尺度精修分类器（MRC）充分挖掘不同分辨率下的结构线索。在三个具有挑战性的跨字体图像检索数据集上的大量实验表明，给定未释读甲骨文，所提CFIRN能有效实现与其它字库字体的准确匹配，从而助力甲骨文破译。
原文摘要 · Abstract (English)
Oracle Bone Inscription (OBI) is the earliest mature writing system in China, which represents a crucial stage in the development of hieroglyphs. Nevertheless, the substantial quantity of undeciphered OBI characters remains a significant challenge for scholars, while conventional methods of ancient script research are both time-consuming and labor-intensive. In this paper, we propose a cross-font image retrieval network (CFIRN) to decipher OBI characters by establishing associations between OBI characters and other script forms, simulating the interpretive behavior of paleography scholars. Concretely, our network employs a siamese framework to extract deep features from character images of various fonts, fully exploring structure clues with different resolutions by multiscale feature integration (MFI) module and multiscale refinement classifier (MRC). Extensive experiments on three challenging cross-font image retrieval datasets demonstrate that, given undeciphered OBI characters, our CFIRN can effectively achieve accurate matches with characters from other gallery fonts, thereby facilitating the deciphering.
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