🖊️ Handwritten Digit Recognition Web App

This is a Flask-based web application that allows users to upload an image of a handwritten digit (0–9), and it predicts the digit using a Convolutional Neural Network (CNN) trained on the MNIST dataset.

🚀 Features

• Upload handwritten digit images (JPG, PNG, etc.)
• Processes the image using PIL and NumPy
• Predicts the digit using a .keras model trained on MNIST
• Displays top-10 class probabilities
• Lightweight Flask backend with HTML frontend
• Saves uploaded images in a dedicated /uploads folder (outside /static)

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📁 Project Structure

Handwritten-Digit-Recognition/
├── app.py                  # Main Flask application
├── digit_model.keras       # Pretrained CNN model (MNIST)
├── uploads/                # Uploaded images (created dynamically)
├── templates/
│   └── index.html          # HTML frontend
├── static/
│   └── style.css           # Optional custom CSS
├── train_model.py          # Script to train model (optional)
└── README.md

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🧠 Tech Stack

• Python 3.8+
• Flask – Web framework
• TensorFlow / Keras – Deep learning
• Pillow (PIL) – Image preprocessing
• NumPy – Array operations

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🛠️ Setup Instructions

1. 📦 Install dependencies

pip install -r requirements.txt

(Or manually install: flask, tensorflow, pillow, numpy)

2. 🧠 Add or train your model

Ensure digit_model.keras is in the root folder. You can use your own trained model or run train_model.py (if available).

3. ▶️ Run the Flask app

python app.py

4. 🌐 Open in browser

Visit: http://127.0.0.1:5000/

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🧪 Example

1. Upload a clear image of a handwritten digit.
2. App processes and resizes it to 28x28 grayscale.
3. The CNN predicts the digit and shows confidence scores.

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🙋 Author

Developed by Mayank Aggarwal (Mayank-01x)

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📄 License

This project is licensed under the MIT License.