一种用于识别未破译甲骨文的跨字体图像检索网络
A Cross-Font Image Retrieval Network for Recognizing Undeciphered Oracle Bone Inscriptions
AI总结：
本文提出跨字体图像检索网络CFIRN，通过孪生框架、多尺度特征集成模块和多尺度细化分类器建立甲骨文与其他字体的关联，实现未破译甲骨文的准确匹配与辅助破译。
摘要：
甲骨文是中国最早的成熟文字系统，代表了象形文字发展中的一个关键阶段。然而，大量未破译的甲骨文字符仍然对学者构成重大挑战，而传统的古文字研究方法既耗时又费力。在本文中，我们提出了一种跨字体图像检索网络（CFIRN），通过建立甲骨文字符与其他文字形式之间的关联来破译甲骨文字符，模拟古文字学者的释读行为。具体而言，我们的网络采用孪生框架从各种字体的字符图像中提取深层特征，通过多尺度特征集成（MFI）模块和多尺度细化分类器（MRC）充分探索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，给定未破译的甲骨文字符，我们的CFIRN能够有效地与其他图库字体中的字符实现准确匹配，从而促进破译工作。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
