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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.

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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.

About

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.

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