A Cross-Font Image Retrieval Network for Recognizing Undeciphered Oracle Bone Inscriptions
This addresses the time-consuming and labor-intensive problem for paleography scholars in deciphering ancient scripts, though it appears incremental as it builds on existing retrieval methods.
The paper tackles the challenge of deciphering undeciphered Oracle Bone Inscription (OBI) characters by proposing a cross-font image retrieval network (CFIRN) that establishes associations between OBI and other script forms, achieving accurate matches in experiments on three cross-font datasets.
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.