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

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

URL: http://arxiv.org/abs/2205.09442v1
Date: Thu, 19 May 2022 09:57:45 GMT
Title: Oracle-MNIST: a Realistic Image Dataset for Benchmarking Machine Learning Algorithms
Authors: Mei Wang, Weihong Deng
Abstract summary: We introduce the Oracle-MNIST dataset, comprising of 28$times $28 grayscale images of 30,222 ancient characters. The training set totally consists of 27,222 images, and the test set contains 300 images per class.
Score: 57.29464116557734
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We introduce the Oracle-MNIST dataset, comprising of 28$\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.

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