# Kannada-MNIST: A new handwritten digits dataset for the Kannada language

arXiv: https://arxiv.org/abs/1908.01242 · HTML: https://mlsanity.com/paper/1908.01242/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2019.

## Built on by (most cited first, 14 of 14)

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- [Multi-script Handwritten Digit Recognition Using Multi-task Learning](https://mlsanity.com/paper/2106.08267/) (2021)
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- [Mitigating covariate shift in non-colocated data with learned parameter priors](https://mlsanity.com/paper/2411.06499/) (2024)
- [BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources](https://mlsanity.com/paper/2604.18423/) (2026)
- [An inclusive review on deep learning techniques and their scope in handwriting recognition](https://mlsanity.com/paper/2404.08011/) (2024)
- [Privacy-Utility Trade-off in Data Publication: A Bilevel Optimization Framework with Curvature-Guided Perturbation](https://mlsanity.com/paper/2509.02048/) (2025)
- [Technical note on Fisher Information for Robust Federated Cross-Validation](https://mlsanity.com/paper/2510.03838/) (2025)
- [BHDD: A Burmese Handwritten Digit Dataset](https://mlsanity.com/paper/2603.21966/) (2026)
- [Rethinking Handwritten Character Recognition](https://mlsanity.com/paper/2609.10572/) (2026)

## Abstract (arXiv)

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.