# Bengali Handwritten Digit Recognition using CNN with Explainable AI

arXiv: https://arxiv.org/abs/2212.12146 · HTML: https://mlsanity.com/paper/2212.12146/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 23 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Visual Interaction with Deep Learning Models through Collaborative Semantic Inference](https://mlsanity.com/paper/1907.10739/) (2019)
- [NumtaDB - Assembled Bengali Handwritten Digits](https://mlsanity.com/paper/1806.02452/) (2018)

## Built on by (most cited first, 2 of 2)

- [Bengali License Plate Recognition: Unveiling Clarity with CNN and GFP-GAN](https://mlsanity.com/paper/2312.10701/) (2023)
- [An Interpretable Deep Learning Approach for Skin Cancer Categorization](https://mlsanity.com/paper/2312.10696/) (2023)

## Abstract (arXiv)

Handwritten character recognition is a hot topic for research nowadays. If we can convert a handwritten piece of paper into a text-searchable document using the Optical Character Recognition (OCR) technique, we can easily understand the content and do not need to read the handwritten document. OCR in the English language is very common, but in the Bengali language, it is very hard to find a good quality OCR application. If we can merge machine learning and deep learning with OCR, it could be a huge contribution to this field. Various researchers have proposed a number of strategies for recognizing Bengali handwritten characters. A lot of ML algorithms and deep neural networks were used in their work, but the explanations of their models are not available. In our work, we have used various machine learning algorithms and CNN to recognize handwritten Bengali digits. We have got acceptable accuracy from some ML models, and CNN has given us great testing accuracy. Grad-CAM was used as an XAI method on our CNN model, which gave us insights into the model and helped us detect the origin of interest for recognizing a digit from an image.