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Area of Science:

  • Digital Humanities
  • Computer Vision
  • Artificial Intelligence

Background:

  • Oracle bone script offers insights into ancient Chinese culture, history, and language.
  • Existing datasets like MNIST lack the complexity of real-world ancient script challenges.

Purpose of the Study:

  • Introduce the Oracle-MNIST dataset for benchmarking pattern classification tasks.
  • Provide a more challenging alternative to MNIST for machine learning research.
  • Facilitate research on classifying ancient scripts with inherent noise and style variations.

Main Methods:

  • Curated a dataset of 30,222 grayscale images (28x28) of ancient Chinese characters from 10 categories.
  • Structured the dataset (27,222 training, 300 test per class) for compatibility with existing systems.
  • Focused on realistic challenges including significant noise and diverse writing styles.

Main Results:

  • The Oracle-MNIST dataset presents a more difficult classification task than the original MNIST.
  • The dataset captures the unique noise and stylistic variations inherent in ancient scripts.
  • Enables direct comparison and integration with existing machine learning classifiers.

Conclusions:

  • Oracle-MNIST serves as a valuable benchmark for pattern classification of historical artifacts.
  • The dataset advances machine learning research by incorporating realistic challenges of ancient data.
  • Promotes further investigation into the interpretation of ancient writing systems through AI.