  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.
