Handwritten Digit Recognition Using Deep Learning

Abstract

The Handwritten Digit Recognition System is a machine learning and deep learning–based project developed to accurately identify handwritten numerical digits from input images. The main objective of this project is to recognize digits ranging from 0 to 9 by using image processing techniques and a Convolutional Neural Network (CNN) model trained on the MNIST dataset. In this system, the handwritten digit image is first captured and preprocessed through steps such as grayscale conversion, resizing, normalization, and noise reduction to improve prediction accuracy. The processed image is then passed to the trained CNN model, which extracts important features and classifies the digit into the corresponding numerical class. The project uses backpropagation for learning, Adam optimizer for efficient weight optimization, and Softmax activation function in the output layer for multi-class classification. The trained model provides high accuracy and fast prediction results, making the system suitable for real-time applications. This project demonstrates the practical implementation of deep learning in image recognition and can be further extended for applications such as automatic form processing, postal code recognition, and bank cheque digit identification.