In this letter, we contribute a multi-language handwritten digit recognition dataset named MNIST-MIX, which is the largest dataset of the same type in terms of both languages and data samples. With the same data format with MNIST, MNIST-MIX can be seamlessly applied in existing studies for handwritten digit recognition. By introducing digits from 10 different languages, MNIST-MIX becomes a more challenging dataset and its imbalanced classification requires a better design of models. We also present the results of applying a LeNet model which is pre-trained on MNIST as the baseline.
Cite
@article{arxiv.2004.03848,
title = {MNIST-MIX: A Multi-language Handwritten Digit Recognition Dataset},
author = {Weiwei Jiang},
journal= {arXiv preprint arXiv:2004.03848},
year = {2021}
}