TITLE: Oracle-MNIST: a Realistic Image Dataset for Benchmarking Machine Learning Algorithms 

ABSTRACT: We introduce the Oracle-MNIST dataset, comprising of 28$\times $28 grayscale
images of 30,222 ancient characters from 10 categories, for benchmarking
pattern classification, with particular challenges on image noise and
distortion. The training set totally consists of 27,222 images, and the test
set contains 300 images per class. Oracle-MNIST shares the same data format
with the original MNIST dataset, allowing for direct compatibility with all
existing classifiers and systems, but it constitutes a more challenging
classification task than MNIST. The images of ancient characters suffer from 1)
extremely serious and unique noises caused by three-thousand years of burial
and aging and 2) dramatically variant writing styles by ancient Chinese, which
all make them realistic for machine learning research. The dataset is freely
available at https://github.com/wm-bupt/oracle-mnist.
We introduce the Oracle-MNIST dataset, comprising of 28$\times $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.
Oracle-MNIST: a Realistic Image Dataset for Benchmarking Machine Learning Algorithms
http://arxiv.org/abs/2205.09442v1