Handwritten Digit Recognition System

Overview

The Handwritten Digit Recognition System is a machine learning project that leverages Convolutional Neural Networks (CNNs) to classify images of handwritten digits from the MNIST dataset. This system achieves high accuracy in recognizing digits from 0 to 9, making it a valuable tool for digit recognition tasks.

Features

• High Accuracy: Utilizes CNNs for effective feature extraction and classification.
• User-Friendly Interface: Allows users to input handwritten digits for real-time prediction.
• Comprehensive Dataset: Trained on the widely-used MNIST dataset with 70,000 images.

Technologies Used

• Python
• TensorFlow/Keras
• NumPy
• Matplotlib

Installation

1. Clone the repository:

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

2. Install the required packages:

   pip install -r requirements.txt

Dataset

The project uses the MNIST dataset, which can be downloaded here.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

License

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