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An open dataset for oracle bone script recognition and decipherment

Pengjie Wang, Kaile Zhang, Xinyu Wang, Shengwei Han, Yongge Liu, Jinpeng Wan, Haisu Guan, Zhebin Kuang, Lianwen Jin, Xiang Bai, Yuliang Liu

TL;DR

This dataset encompasses 77,064 images of 1,588 individual deciphered characters and 62,989 images of 9,411 undeciphered characters, with a total of 140,053 images, compiled from diverse sources, and the hope is that this dataset could inspire and assist future research in deciphering those unknown OBCs.

Abstract

Oracle bone script, one of the earliest known forms of ancient Chinese writing, presents invaluable research materials for scholars studying the humanities and geography of the Shang Dynasty, dating back 3,000 years. The immense historical and cultural significance of these writings cannot be overstated. However, the passage of time has obscured much of their meaning, presenting a significant challenge in deciphering these ancient texts. With the advent of Artificial Intelligence (AI), employing AI to assist in deciphering Oracle Bone Characters (OBCs) has become a feasible option. Yet, progress in this area has been hindered by a lack of high-quality datasets. To address this issue, this paper details the creation of the HUST-OBC dataset. This dataset encompasses 77,064 images of 1,588 individual deciphered characters and 62,989 images of 9,411 undeciphered characters, with a total of 140,053 images, compiled from diverse sources. The hope is that this dataset could inspire and assist future research in deciphering those unknown OBCs. All the codes and datasets are available at https://github.com/Yuliang-Liu/Open-Oracle.

An open dataset for oracle bone script recognition and decipherment

TL;DR

This dataset encompasses 77,064 images of 1,588 individual deciphered characters and 62,989 images of 9,411 undeciphered characters, with a total of 140,053 images, compiled from diverse sources, and the hope is that this dataset could inspire and assist future research in deciphering those unknown OBCs.

Abstract

Oracle bone script, one of the earliest known forms of ancient Chinese writing, presents invaluable research materials for scholars studying the humanities and geography of the Shang Dynasty, dating back 3,000 years. The immense historical and cultural significance of these writings cannot be overstated. However, the passage of time has obscured much of their meaning, presenting a significant challenge in deciphering these ancient texts. With the advent of Artificial Intelligence (AI), employing AI to assist in deciphering Oracle Bone Characters (OBCs) has become a feasible option. Yet, progress in this area has been hindered by a lack of high-quality datasets. To address this issue, this paper details the creation of the HUST-OBC dataset. This dataset encompasses 77,064 images of 1,588 individual deciphered characters and 62,989 images of 9,411 undeciphered characters, with a total of 140,053 images, compiled from diverse sources. The hope is that this dataset could inspire and assist future research in deciphering those unknown OBCs. All the codes and datasets are available at https://github.com/Yuliang-Liu/Open-Oracle.
Paper Structure (19 sections, 8 figures, 4 tables)

This paper contains 19 sections, 8 figures, 4 tables.

Table of Contents

  1. Background & Summary
  2. Methods
  3. Data Records
  4. Technical Validation
  5. Licenses
  6. Usage Notes
  7. Code Availability
  8. Acknowledgements
  9. Author contributions statement
  10. Competing interests

Figures (8)

  • Figure 1: 3000-year-old Shang Dynasty oracle bones. They were all unearthed at the Huayuanzhuang East in Yinxu, China, and are currently housed at the Anyang workstation of the Institute of Archaeology, Chinese Academy of Social Sciences. These oracle bones date back to the reign of King Wu Ding of the Shang Dynasty.
  • Figure 2: Flowchart of building the HUST-OBC dataset.
  • Figure 3: Extraction of OBC images from books. New Compilation of Oracle Bone Scripts (left)X and Oracle Bone Script: Six Digit Numerical Code (right)L.
  • Figure 4: Screenshots of example website pages from YinQiWenYuan and GuoXueDaShi.
  • Figure 5: Schematic of the proposed category assigner.
  • ...and 3 more figures