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Handwritten Digit Recognition Using Convolutional Neural Networks: A Case Study on the MNIST Dataset

Chirag Ananda Kumar, Gajanan M Naik

Abstract


The recognition of handwritten digits is a fundamental task in computer vision and pattern recognition. This study focused on classifying handwritten digits (0–9) from the MNIST dataset using a Convolutional Neural Network (CNN). CNNs can automatically identify spatial and hierarchical features within image data. The developed CNN model achieved a test accuracy of 99.27%, surpassing traditional methods such as Support Vector Machines (SVM) and K-nearest neighbors. The results show that deep learning-based feature extraction provides a strong and efficient framework for practical uses like postal automation, bank check processing, and digital document verification.


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