用结构约束的字体生成法,帮专家破解难辨的甲骨文。
OracleFusion: Assisting the Decipherment of Oracle Bone Script with Structurally Constrained Semantic Typography
- 分两阶段:先分析字形结构,再生成带语义的矢量字体。
- 在1600个已解字上测试,新方法在可读性和美观度上均更优。
- 适合古文字研究者,能给出未见字的专家级解读建议。
作为最早期的古代语言之一,甲骨文(OBS)承载了古代文明的文化记录与思想表达。尽管已发现约4500个甲骨文字,仅有约1600个被成功破译。剩余未解字符因结构复杂、意象抽象,极大增加了解读难度。本文提出一种名为OracleFusion的两阶段语义排版框架。第一阶段利用增强空间感知推理(SAR)的多模态大模型(MLLM),分析甲骨文字形结构并定位关键部件;第二阶段引入甲骨结构向量融合(OSVF),结合字形约束与笔画保持约束,生成语义丰富的矢量字体。该方法保持字形客观完整性,提供视觉增强表示,显著提升可读性与美学质量。大量定性与定量实验表明,OracleFusion在语义准确性、视觉吸引力和字形保持方面均优于现有最优模型。此外,它能对未见甲骨文字符提供类专家见解,是推进甲骨文破译的重要工具。
原文摘要 · Abstract (English)
As one of the earliest ancient languages, Oracle Bone Script (OBS) encapsulates the cultural records and intellectual expressions of ancient civilizations. Despite the discovery of approximately 4,500 OBS characters, only about 1,600 have been deciphered. The remaining undeciphered ones, with their complex structure and abstract imagery, pose significant challenges for interpretation. To address these challenges, this paper proposes a novel two-stage semantic typography framework, named OracleFusion. In the first stage, this approach leverages the Multimodal Large Language Model (MLLM) with enhanced Spatial Awareness Reasoning (SAR) to analyze the glyph structure of the OBS character and perform visual localization of key components. In the second stage, we introduce Oracle Structural Vector Fusion (OSVF), incorporating glyph structure constraints and glyph maintenance constraints to ensure the accurate generation of semantically enriched vector fonts. This approach preserves the objective integrity of the glyph structure, offering visually enhanced representations that assist experts in deciphering OBS. Extensive qualitative and quantitative experiments demonstrate that OracleFusion outperforms state-of-the-art baseline models in terms of semantics, visual appeal, and glyph maintenance, significantly enhancing both readability and aesthetic quality. Furthermore, OracleFusion provides expert-like insights on unseen oracle characters, making it a valuable tool for advancing the decipherment of OBS.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。