Abstract :

Handwritten Digit Recognition Has Become An Important Research Area In Computer Vision And Machine Learning Due To Its Wide Range Of Applications In Automated Document Processing, Postal Code Recognition, Bank Cheque Verification, And Intelligent Data Entry Systems. Accurate Recognition Of Handwritten Digits Remains Challenging Because Of Variations In Writing Styles, Stroke Thickness, Orientation, And Image Quality. This Paper Presents An Intelligent Handwritten Digit Recognition System Based On Convolutional Neural Networks (CNNs) For Accurate Classification Of Numerical Digits From Handwritten Images. The Proposed System Utilizes The Modified National Institute Of Standards And Technology (MNIST) Dataset, Which Contains Thousands Of Labeled Handwritten Digit Images. The Framework Incorporates Image Preprocessing Techniques Such As Normalization, Grayscale Conversion, Resizing, And Noise Reduction To Enhance Image Quality Before Classification. A Deep CNN Architecture Is Employed To Automatically Extract Relevant Features From Input Images And Perform Multi-class Digit Classification With High Accuracy. The System Is Implemented Using Python, TensorFlow, Keras, NumPy, OpenCV, And Flask, Providing An Efficient And Scalable Environment For Model Training And Deployment. Experimental Results Demonstrate That The Proposed CNN-based Approach Achieves High Recognition Accuracy While Maintaining Computational Efficiency. The Model Effectively Handles Variations In Handwriting And Outperforms Traditional Machine Learning Techniques That Rely On Handcrafted Feature Extraction. The Proposed Framework Demonstrates How Deep Learning-based Image Recognition Systems Can Significantly Improve The Accuracy, Reliability, And Automation Of Handwritten Digit Classification Tasks. The Study Highlights The Effectiveness Of Convolutional Neural Networks In Learning Complex Visual Patterns And Presents A Practical Solution For Real-world Handwritten Digit Recognition Applications.

Published:

11-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

484-499

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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