MNIST-Digit-Recognition

This project focuses on the development of a machine learning model designed to recognize and predict handwritten digits using the MNIST dataset

Overview

This repository contains the code and resources for building a machine learning model to recognize handwritten digits from the MNIST dataset. The goal of this project is to create a robust and accurate model capable of predicting digits from 0 to 9.

Dataset

The MNIST dataset consists of 60,000 training images and 10,000 test images of handwritten digits. Each image is 28x28 pixels in size.

Project Structure

• data/: Contains the dataset (training and test sets).
• notebooks/: Jupyter notebooks for data exploration, preprocessing, and model training.
• src/: Source code for the project, including data processing and model definition scripts.
• models/: Saved models and checkpoints.
• results/: Evaluation results and performance metrics.

Installation

To run this project, you need Python 3.7+ and the following dependencies:

pip install -r requirements.txt

Usage

Clone the repository:

git clone https://github.com/yourusername/mnist-digit-recognition.git
cd mnist-digit-recognition

Download the MNIST dataset:

The dataset will be automatically downloaded when you run the notebook or script.

Run the Jupyter notebook:

jupyter notebook notebooks/train.ipynb

Train the model:

You can train the model by following the steps in the Jupyter notebook.

Model

The model used in this project is a convolutional neural network (CNN) implemented using TensorFlow and Keras.

Run the Application

Start the Flask app:

python app.py

Access the application in your web browser at http://localhost:5000/.

Results

The final model achieves an accuracy of 98.99% on the MNIST test set.

After running app.py, navigate to http://localhost:5000/ to access a canvas where you can draw digits. Submit your drawing to see how accurately my model predicts the digit.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any changes or improvements.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• The creators and maintainers of the MNIST dataset.
• TensorFlow and Keras for providing powerful tools for deep learning.
• The open-source community for continuous support and contributions.