This project demonstrates the use of deep learning to classify handwritten digits using the MNIST dataset. A neural network model is trained to recognize digits from zero to nine based on pixel level image data.
The project showcases a complete deep learning workflow including data preprocessing model design training evaluation and prediction. It highlights how neural networks can effectively learn patterns from image data.
Project Objective
The objective of this project is to build a reliable handwritten digit classification system using a neural network. The project aims to demonstrate how image data can be transformed and used to train deep learning models for accurate classification tasks.
This project also serves as a foundation for understanding more advanced computer vision applications.
What This Project Does
The model processes grayscale images of handwritten digits and converts them into numerical representations suitable for neural network training.
Each image is analyzed and classified into one of ten digit classes. The system learns digit patterns by adjusting network weights during training and produces accurate predictions on unseen images.
Dataset Description
The MNIST dataset consists of grayscale images with a resolution of twenty eight by twenty eight pixels. Each image represents a handwritten digit from zero to nine.
This dataset is widely used as a benchmark for evaluating image classification and deep learning models due to its simplicity and reliability.
Model Architecture
The neural network is built using the Keras Sequential API. It includes a flatten layer followed by fully connected dense layers with nonlinear activation functions.
The output layer uses softmax activation to generate probability scores for each digit class. This architecture enables effective multi class classification.
Training and Evaluation
The model is trained on a labeled dataset using multiple training epochs. During training the network learns to identify digit patterns through backpropagation.
Model performance is evaluated on a separate test dataset to measure generalization on unseen handwritten digit images.
Key Highlights
- Deep learning based handwritten digit classification
- Uses MNIST benchmark dataset
- Image normalization and preprocessing applied
- Neural network implemented with Keras
- End to end training evaluation and prediction pipeline
Technologies Used
Python for implementation
NumPy for numerical operations
TensorFlow and Keras for neural network modeling
Matplotlib for visualization
Scikit learn for evaluation utilities
Use Case and Applications
This project demonstrates how deep learning models can be used for optical character recognition and digit recognition systems. Similar approaches are used in applications such as document processing postal code recognition and automated data entry systems.