# MNIST-MIX: A Multi-language Handwritten Digit Recognition Dataset

arXiv: https://arxiv.org/abs/2004.03848 · HTML: https://mlsanity.com/paper/2004.03848/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [HCR-Net: A deep learning based script independent handwritten character recognition network](https://mlsanity.com/paper/2108.06663/) (2021)
- [Active Data Discovery: Mining Unknown Data using Submodular Information Measures](https://mlsanity.com/paper/2206.08566/) (2022)
- [Multi-script Handwritten Digit Recognition Using Multi-task Learning](https://mlsanity.com/paper/2106.08267/) (2021)

## Abstract (arXiv)

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.