In this paper, we disseminate a new handwritten digits-dataset, termed
Kannada-MNIST, for the Kannada script, that can potentially serve as a direct
drop-in replacement for the original MNIST dataset. In addition to this
dataset, we disseminate an additional real world handwritten dataset (with
$10k$ images), which we term as the Dig-MNIST dataset that can serve as an
out-of-domain test dataset. We also duly open source all the code as well as
the raw scanned images along with the scanner settings so that researchers who
want to try out different signal processing pipelines can perform end-to-end
comparisons. We provide high level morphological comparisons with the MNIST
dataset and provide baselines accuracies for the dataset disseminated. The
initial baselines obtained using an oft-used CNN architecture ($96.8\%$ for the
main test-set and $76.1\%$ for the Dig-MNIST test-set) indicate that these
datasets do provide a sterner challenge with regards to generalizability than
MNIST or the KMNIST datasets. We also hope this dissemination will spur the
creation of similar datasets for all the languages that use different symbols
for the numeral digits.

本文介绍了一种新的手写数字数据集 Kannada-MNIST，可作为原 MNIST 数据集的直接替代品，并提供了另外一个真实世界的手写数据集 Dig-MNIST，以作为领域外测试数据集。我们公开了所有代码以及原始扫描图像与扫描设置，这使得研究人员可以进行端到端比较。所得到的基线准确率表明，这些数据集确实提供了一个比 MNIST 或 KMNIST 数据集更具挑战性的普适性挑战。