A Cross-Font Image Retrieval Network for Recognizing Undeciphered Oracle Bone Inscriptions

TL;DR

This paper introduces a cross-font image retrieval network that helps decipher undeciphered Oracle Bone Inscriptions by matching characters across different fonts using deep learning techniques.

Contribution

It proposes a novel siamese network with multiscale feature integration and refinement for cross-font OBI character retrieval, simulating paleographic interpretation.

Findings

Effective retrieval accuracy on three datasets

Facilitates deciphering of undeciphered OBI characters

Outperforms existing methods in cross-font matching

Abstract

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.