Handwritten Digit Recognition

This project focuses on recognizing handwritten digits using deep learning techniques. Leveraging convolutional neural networks (CNNs), the system can accurately classify digits from images of handwritten numbers, enabling applications such as automated form processing and digit recognition in various contexts.

Features

• Accurate digit recognition from handwritten images.
• Supports digit classification from 0 to 9.
• Real-time prediction with high accuracy.
• User-friendly web interface for inputting handwritten digits.

Installation

To set up the handwritten digit recognition system, follow these steps:

1. Clone the Repository

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

2. Set Up Virtual Environment (Optional but Recommended)

   python3 -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install Required Packages

   pip install -r requirements.txt

   The key dependencies include:

   • tensorflow
   • keras
   • numpy
   • pillow
4. Download Pretrained Models

   Download the pretrained model files and place them in the models directory:

   /models/pretrained_digit_model.h5

Usage

To recognize a handwritten digit, use the following command:

python recognize_digit.py --image path/to/handwritten_digit.png

The script will process the image and output the recognized digit.

Model Description

The project utilizes a Convolutional Neural Network (CNN) trained on the MNIST dataset of handwritten digits. The model architecture includes:

• Convolutional layers for feature extraction.
• Pooling layers to reduce dimensionality.
• Fully connected layers for classification.

The model is optimized to achieve high accuracy in recognizing digits from handwritten images.

How It Works

1. Preprocessing: The input image is resized and normalized for consistency.
2. Prediction: The preprocessed image is fed into the CNN model to classify the digit.
3. Postprocessing: The model's output is interpreted to display the recognized digit.

Contributing

We welcome contributions to this project. To contribute:

1. Fork the repository.
2. Create a new branch: git checkout -b feature-branch.
3. Commit your changes: git commit -m 'Add new feature'.
4. Push to the branch: git push origin feature-branch.
5. Create a pull request.

License

This project is licensed under the MIT License.