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This research applies Neural Ordinary Differential Equations to model the continuous evolution of Chinese characters across dynasties, addressing limitations in single-period reference methods for deciphering Oracle Bone Inscriptions. By treating script development as a manifold space transition problem, the proposed framework offers a novel computational approach to historical linguistics and character recognition that relies on advanced machine learning techniques rather than incidental AI features.
Tracing the Evolution of Oracle Bone Characters Across Three Millennia
arXiv:2609.35674v1 Announce Type: new Abstract: Of the approximately 4,500 Oracle Bone Inscription (OBI) characters discovered from the Shang dynasty, only about 1,600 have been deciphered. Many computational approaches compare OBI with glyphs from one historical period at a time. However, during the evolution of Chinese characters, significant structural or semantic changes often occur in uncertain dynasties. A single-period reference may be insufficient when relevant forms change substantially between observed eras. Therefore, we propose the \textbf{Manifold-based Script Evolution Framework (MSEF)}, a framework that models the evolution series (OBI, Bronze, Seal, Clerical, Regular) of Chinese characters as the continual evolution of a manifold space. MSEF represents each character as an era-specific manifold point and learns continuous inter-era transition rules via Neural Ordinary Differential Equations. Both manifold space and transition dynamics can be trained end-to-end through character evolution pairs across any two eras.
Abstract:Of the approximately 4,500 Oracle Bone Inscription (OBI) characters discovered from the Shang dynasty, only about 1,600 have been deciphered. Many computational approaches compare OBI with glyphs from one historical period at a time. However, during the evolution of Chinese characters, significant structural or semantic changes often occur in uncertain dynasties. A single-period reference may be insufficient when relevant forms change substantially between observed eras. Therefore, we propose the \textbf{Manifold-based Script Evolution Framework (MSEF)}, a framework that models the evolution series (OBI, Bronze, Seal, Clerical, Regular) of Chinese characters as the continual evolution of a manifold space. MSEF represents each character as an era-specific manifold point and learns continuous inter-era transition rules via Neural Ordinary Differential Equations. Both manifold space and transition dynamics can be trained end-to-end through character evolution pairs across any two eras.
Computation and Language (cs.CL)
arXiv:2609.35674 [cs.CL]
(or arXiv:2609.35674v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.35674 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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