Bengali Handwritten Digit Recognition using CNN with Explainable AI

TL;DR

This paper explores Bengali handwritten digit recognition using CNN and integrates Explainable AI techniques to enhance model interpretability, achieving high accuracy and providing insights into the decision-making process.

Contribution

It introduces the application of CNN combined with Grad-CAM for Bengali digit recognition, offering both high accuracy and model interpretability.

Findings

CNN achieved high testing accuracy.

Grad-CAM provided insights into model decisions.

ML models showed acceptable accuracy.

Abstract

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.