Handwritten Digit Recognition Project Guide

Recognizing handwritten digits accurately is a classic problem in computer vision and pattern recognition. Traditional machine learning models require manual feature extraction, but deep learning models like Convolutional Neural Networks (CNNs) can automatically learn features directly from pixel data. In real-world applications like postal automation, bank cheque processing, and document scanning, digit recognition plays a critical role. Hence, building a system that reliably identifies handwritten numbers is both academically valuable and practically useful.

CNNs are specifically designed for working with image data. They apply filters to the input images to extract important features such as edges, shapes, and textures, which are essential for recognizing handwritten digits. With layers of convolution, pooling, and activation functions, CNNs automatically build increasingly abstract representations of the input, leading to high classification accuracy. This project provides a practical experience with deep learning, image preprocessing, and model optimization.

The system receives a grayscale image of a handwritten digit and passes it through multiple layers of convolutions and poolings. Features extracted at each layer represent increasingly complex patterns, helping the final dense layers classify the image into one of the ten digit classes (0–9). By using techniques like data augmentation, dropout, and batch normalization, the model’s performance can be significantly improved for real-world datasets like MNIST.

• Collect and preprocess image datasets like MNIST for digit images.
• Design a CNN architecture with convolutional and pooling layers followed by dense layers.
• Train the model using cross-entropy loss and an optimizer like Adam.
• Evaluate the model using accuracy, confusion matrix, and visualizations.
• Deploy the trained model into an application that accepts image input and predicts digits.

• TensorFlow Tutorials for Beginners
• PyTorch Tutorials
• MNIST Dataset Download