Handwritten Digit Recognition using Artificial Neural Networks (ANN)

Handwritten digit recognition serves as a classic introductory project in deep learning, typically using the MNIST dataset as a benchmark. The Artificial Neural Network (ANN) implemented for this task primarily consists of an input layer, hidden layers, and an output layer. The core concept involves transforming raw pixel features through multi-level nonlinear transformations into higher-level digit category features.

The input layer receives 28x28 pixel handwritten digit images, flattening them into a 784-dimensional vector. Hidden layers generally comprise several fully connected (dense) layers, each containing multiple neurons that introduce nonlinearity through activation functions like ReLU. The output layer contains 10 neurons corresponding to digit classes 0-9, utilizing the Softmax function to convert outputs into probability distributions.

The training process employs backpropagation algorithm, calculating cross-entropy loss between predictions and true labels, then optimizing network parameters through gradient descent. To enhance generalization capability, Dropout layers can be added to prevent overfitting, or batch normalization can be implemented to accelerate convergence.

This project's key value lies in demonstrating ANN's fundamental principles for image classification: automatically learning digit stroke patterns from low-level pixel features. Although modern convolutional neural networks (CNNs) achieve better performance on this task, fully connected ANNs remain the preferred introductory case for understanding deep learning fundamentals due to their straightforward architecture.