Handwritten Digit Recognition

A beginner-friendly deep learning project using TensorFlow and the MNIST dataset to recognize handwritten digits (0-9). Includes an interactive Gradio interface to draw digits and see real-time predictions with confidence scores.

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Features

• MNIST dataset
• Draw digits in browser using Gradio
• Real-time predictions with confidence level
• Modular structure for training, testing, and inference
• Model is saved and loaded using .h5 format

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Demo

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Project Structure

handwritten-digit-recognition/
├── explore_mnist.py                    # MNIST dataset exploration
├── mnist_model_train.ipynb            # Model training notebook
├── model_test.py                      # Model testing script
├── mnist_gradio_digit_classifier.ipynb # Gradio interface for prediction
├── LICENSE
└── README.md

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Installation

1. Clone the repository:

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

2. Install dependencies:

pip install tensorflow gradio numpy matplotlib

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Train the Model

If you'd like to train the model yourself, open the notebook:

jupyter notebook mnist_model_train.ipynb

After training, the model is saved as:

model.save("mnist_model.h5")

This .h5 file includes:

• Model architecture
• Trained weights
• Optimizer state
• Training configuration

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Run the Gradio App

Launch the web interface to draw digits:

jupyter notebook mnist_gradio_digit_classifier.ipynb

Then click the Gradio link to open the digit prediction UI in your browser.

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Sample Output

Prediction: 6
Confidence: 80.1%

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License

This project is licensed under the MIT License.

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Author

Developed by rukiyeberna