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Digitizer

A Digit Recognition Flask-based Web Application that utilizes a Convolutional Neural Network (CNN) model, trained on the MNIST dataset, to recognize handwritten digits drawn by the user. It showcases the integration of a PyTorch machine learning model with a web frontend, allowing for real-time digit recognition.

Features

  • Real-time Digit Recognition: Users can draw a digit on the canvas, and the application predicts which digit it is.
  • PyTorch Integration: Utilizes a CNN model built and trained using PyTorch, demonstrating how machine learning models can be integrated into web applications.
  • Flask Backend: A lightweight Flask application serves as the backend, handling image processing and model inference.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

Before you begin, ensure you have the following installed:

  • Python 3.6 or higher
  • pip and venv

Installation

  1. Clone the repository:

    git clone https://github.com/txhno/digit-recognition-app.git

  2. Navigate to the project directory:

    cd digit-recognition-app

  3. Create a virtual environment and activate it:

    python3 -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

  4. Install the required packages:

    pip install -r requirements.txt

Training the Model

Before running the Flask application, you need to train the CNN model using the MNIST dataset. This step creates the mnist_cnn.pth file, which the Flask app uses to predict digits.

  1. Ensure you are still in the project's root directory.
  2. Run the training script: 
    python train.py
    This script will train the model and save it to the file mnist_cnn.pth in the project directory.

Usage

  1. Run the Flask application:

    python app.py

  2. Open a web browser and navigate to http://localhost:5000 to access the application.

  3. Use the canvas to draw a digit (0-9) and click "Submit" to see the model's prediction.

Built With

  • PyTorch - The machine learning framework used for building the CNN model.
  • Flask - The web framework used for the application backend.
  • HTML/CSS/JavaScript - Used for the frontend interface.

Authors

  • Roshan Warrier - Initial work - txhno

License

This project is licensed under the MIT License - see the LICENSE.md file for details.