# Oracle-MNIST: a Dataset of Oracle Characters for Benchmarking Machine Learning Algorithms

arXiv: https://arxiv.org/abs/2205.09442 · HTML: https://mlsanity.com/paper/2205.09442/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 2 of 2)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)

## Built on by (most cited first, 4 of 4)

- [Masked Modeling for Self-supervised Representation Learning on Vision and Beyond](https://mlsanity.com/paper/2401.00897/) (2023)
- [LR-Net: A Block-based Convolutional Neural Network for Low-Resolution Image Classification](https://mlsanity.com/paper/2207.09531/) (2022)
- [Vision Eagle Attention: a new lens for advancing image classification](https://mlsanity.com/paper/2411.10564/) (2024)
- [Enhancing Oracle Bone Inscription Recognition via Multi-Scale Layer Attention](https://mlsanity.com/paper/2607.00057/) (2026)

## Abstract (arXiv)

We introduce the Oracle-MNIST dataset, comprising of 28× 28 grayscale images of 30,222 ancient characters from 10 categories, for benchmarking pattern classification, with particular challenges on image noise and distortion. The training set totally consists of 27,222 images, and the test set contains 300 images per class. Oracle-MNIST shares the same data format with the original MNIST dataset, allowing for direct compatibility with all existing classifiers and systems, but it constitutes a more challenging classification task than MNIST. The images of ancient characters suffer from 1) extremely serious and unique noises caused by three-thousand years of burial and aging and 2) dramatically variant writing styles by ancient Chinese, which all make them realistic for machine learning research. The dataset is freely available at https://github.com/wm-bupt/oracle-mnist.