mlsanityData to 7 Oct 2026

An open dataset for the evolution of oracle bone characters: EVOBC

arXiv 2401.12467 ↗First posted 23 Jan 2024cs.AIcited by 15

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Abstract (arXiv)

The earliest extant Chinese characters originate from oracle bone inscriptions, which are closely related to other East Asian languages. These inscriptions hold immense value for anthropology and archaeology. However, deciphering oracle bone script remains a formidable challenge, with only approximately 1,600 of the over 4,500 extant characters elucidated to date. Further scholarly investigation is required to comprehensively understand this ancient writing system. Artificial Intelligence technology is a promising avenue for deciphering oracle bone characters, particularly concerning their evolution. However, one of the challenges is the lack of datasets mapping the evolution of these characters over time. In this study, we systematically collected ancient characters from authoritative texts and websites spanning six historical stages: Oracle Bone Characters - OBC (15th century B.C.), Bronze Inscriptions - BI (13th to 221 B.C.), Seal Script - SS (11th to 8th centuries B.C.), Spring and Autumn period Characters - SAC (770 to 476 B.C.), Warring States period Characters - WSC (475 B.C. to 221 B.C.), and Clerical Script - CS (221 B.C. to 220 A.D.). Subsequently, we constructed an extensive dataset, namely EVolution Oracle Bone Characters (EVOBC), consisting of 229,170 images representing 13,714 distinct character categories. We conducted validation and simulated deciphering on the constructed dataset, and the results demonstrate its high efficacy in aiding the study of oracle bone script. This openly accessible dataset aims to digitalize ancient Chinese scripts across multiple eras, facilitating the decipherment of oracle bone script by examining the evolution of glyph forms.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 3 of its 5 references that are in the map. First posted on arXiv 23 Jan 2024.

Builds on

3 of 5 references.

References · 3

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2021Swin Transformer V2: Scaling Up Capacity and Resolutioncited by 1,215
  3. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 2; 2026: 9.

15 papers.

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  1. 2024OBI-Bench: Can LMMs Aid in Study of Ancient Script on Oracle Bones?cited by 22
  2. 2024Deciphering Oracle Bone Language with Diffusion Modelscited by 18
  3. 2024OracleSage: Towards Unified Visual-Linguistic Understanding of Oracle Bone Scripts through Cross-Modal Knowledge Fusioncited by 10
  4. 2024Puzzle Pieces Picker: Deciphering Ancient Chinese Characters with Radical Reconstructioncited by 8
  5. 2025Mitigating Long-tail Distribution in Oracle Bone Inscriptions: Dataset, Model, and Benchmarkcited by 5
  6. 2026Chronicles-OCR: A Cross-Temporal Perception Benchmark for the Evolutionary Trajectory of Chinese Characterscited by 5
  7. 2025MCCD: A Multi-Attribute Chinese Calligraphy Character Dataset Annotated with Script Styles, Dynasties, and Calligrapherscited by 3
  8. 2026AMR-CCR: Anchored Modular Retrieval for Continual Chinese Character Recognitioncited by 0
  9. 2026OracleAnalyser: Analysing Implicit Semantics of Oracle Bone Scripts through MLLMs with Post-trainingcited by 0
  10. 2026Beyond Single Character: Evaluating MLLMs for Sentence-Level Oracle Bone Inscription Understandingcited by 0
  11. 2026Enhancing Oracle Bone Inscription Recognition via Multi-Scale Layer Attentioncited by 0
  12. 2026HCSU: A Dataset and Benchmark for Fine-Grained Historical Calligraphy Style Understandingcited by 0
3 more
  1. 2026AlphaOracle: Oracle bone script decipherment via human-workflow-inspired deep learningcited by 0
  2. 2026JieZi: A Large-Scale Expert-Audited Dataset and Benchmark for Ancient Chinese Character Exegesiscited by 0
  3. 2026FROD: Feature Matching Residual Denoising Oracle Bone Deciphercited by 0