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🖊️ DigitRecognizer

A handcrafted neural network built from scratch using NumPy to classify handwritten digits from the MNIST dataset. Perfect for learning and experimenting with core neural network concepts like forward propagation, backpropagation, and gradient descent.

🚀 Features

✅ Custom Neural Network
A single hidden-layer network with sigmoid activation functions, trained entirely from scratch.

✅ MNIST Dataset Integration
Seamlessly loads and preprocesses the MNIST dataset for efficient training and testing.

✅ Interactive Testing
Test the model on MNIST data or your custom handwritten digits (custom.png).

✅ Training Visualization
Monitor the model's performance through loss and accuracy updates after every epoch.

✅ Minimal Dependencies
Uses only Python, NumPy, and Matplotlib—no heavy frameworks required!

📂 Project Structure

DigitRecognizer/
│
├── main.py         # Main script for training and testing
├── test.py         # Interactive testing script
├── utils.py        # Utility functions for loading the MNIST dataset
├── mnist.npz       # Preloaded MNIST dataset
├── custom.png      # Custom image for testing
├── tests/
│   ├── test2.png   # Additional test image 1
│   └── test3.png   # Additional test image 2
├── demo/
│   └── demo        # Directory for demo files
├── .gitignore      # Git ignore file for unnecessary files
├── requirements.txt # List of required Python packages
└── README.md       # Repository documentation

🛠️ Getting Started

1. Clone the Repository

git clone https://github.com/MansurPro/DigitRecognizer.git
cd DigitRecognizer

2. Install Dependencies

Ensure you have Python installed, then install NumPy and Matplotlib:

pip install -r requirements.txt

3. Train the Model

Run the main.py script to train the neural network on the MNIST dataset:

python main.py

4. Test the Model

Use test.py to interactively test the model with MNIST data or custom images:

python test.py

🎨 Preview

Training Visualization:

Training Visualization

Interactive Testing (success):

Interactive Testing

Interactive Testing (fail):

Interactive Testing

📊 How It Works

  1. Input Layer: Processes 784-pixel flattened grayscale images.
  2. Hidden Layer: Applies sigmoid activation for feature extraction.
  3. Output Layer: Outputs predictions using sigmoid activation.
  4. Backpropagation: Optimizes weights and biases to minimize error using gradient descent.

🧑‍💻 Contributions

We welcome contributions! Feel free to fork this repository, open issues, or submit pull requests.

📜 License

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

🙌 Acknowledgments

Special thanks to the creators of the MNIST dataset and the open-source community for their resources and support.