# NumtaDB - Assembled Bengali Handwritten Digits

arXiv: https://arxiv.org/abs/1806.02452 · HTML: https://mlsanity.com/paper/1806.02452/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2018.

## Built on by (most cited first, 8 of 8)

- [Two Decades of Bengali Handwritten Digit Recognition: A Survey](https://mlsanity.com/paper/2206.02234/) (2022)
- [Performance Analysis of Few-Shot Learning Approaches for Bangla Handwritten Character and Digit Recognition](https://mlsanity.com/paper/2506.00447/) (2025)
- [Efficient approach of using CNN based pretrained model in Bangla handwritten digit recognition](https://mlsanity.com/paper/2209.13005/) (2022)
- [Bengali Handwritten Digit Recognition using CNN with Explainable AI](https://mlsanity.com/paper/2212.12146/) (2022)
- [A Classical Approach to Handcrafted Feature Extraction Techniques for Bangla Handwritten Digit Recognition](https://mlsanity.com/paper/2201.10102/) (2022)
- [Unicode Normalization and Grapheme Parsing of Indic Languages](https://mlsanity.com/paper/2306.01743/) (2023)
- [BHDD: A Burmese Handwritten Digit Dataset](https://mlsanity.com/paper/2603.21966/) (2026)
- [WeightCLIP: Aligning Datasets and Models for Weight Space Learning](https://mlsanity.com/paper/2607.03551/) (2026)

## Abstract (arXiv)

To benchmark Bengali digit recognition algorithms, a large publicly available dataset is required which is free from biases originating from geographical location, gender, and age. With this aim in mind, NumtaDB, a dataset consisting of more than 85,000 images of hand-written Bengali digits, has been assembled. This paper documents the collection and curation process of numerals along with the salient statistics of the dataset.