Sundial-GAN: A Cascade Generative Adversarial Networks Framework for Deciphering Oracle Bone Inscriptions

Xiang Chang, Fei Chao*, Changjing Shang, Qiang Shen

*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

12 Dyfyniadau (Scopus)

Crynodeb

Oracle Bone Inscription (OBI) is an early hieroglyph in China, which is the most famous ancient writing system in the world. However, only a small number of OBI characters have been fully deciphered today. Chinese characters have different forms in different historical stages; therefore, it is very difficult to directly translate OBI characters to modern Chinese characters due to the long historic evolutionary process. In this paper, we propose a cascade generative adversarial networks (GAN) framework for deciphering OBI characters, named "Sundial-GAN", which is a cascaded structure to simulate Chinese characters' evolutionary process from an OBI character to its potential modern Chinese character. We select four representative stages in the evolutionary process of OBI, each of which is implemented by an individual GAN structure based on the characteristics of each evolutionary stage. These structures are cascaded in sequence to accurately simulate the Chinese characters' evolutionary process. For each input OBI character, Sundial-GAN can successfully generate the input's different forms at the four historical stages. Extensive experiments and comparisons demonstrate that generated characters at each stage have high similarities with real existing characters; therefore, the proposed method can significantly improve the efficiency and accuracy of OBI deciphering for archaeological researchers. Compared to direct image-to-image translation methods, our approach allows for a smoother translation process, a better grasp of details, and more effective avoiding random mappings in GANs.

Iaith wreiddiolSaesneg
TeitlMM 2022
Is-deitlProceedings of the 30th ACM International Conference on Multimedia
CyhoeddwrAssociation for Computing Machinery
Tudalennau1195-1203
Nifer y tudalennau9
ISBN (Electronig)9781450392037
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 10 Hyd 2022
Digwyddiad30th ACM International Conference on Multimedia, MM 2022 - Lisboa, Portiwgal
Hyd: 10 Hyd 202214 Hyd 2022

Cyfres gyhoeddiadau

EnwMM 2022 - Proceedings of the 30th ACM International Conference on Multimedia

Cynhadledd

Cynhadledd30th ACM International Conference on Multimedia, MM 2022
Gwlad/TiriogaethPortiwgal
DinasLisboa
Cyfnod10 Hyd 202214 Hyd 2022

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