Hand-Written-Digit-Recognition

This project is a demonstration of a Hand Written Digit Recognition system. It uses machine learning techniques to recognize and classify handwritten digits. Fed and trained on MNIST dataset using 5 layer convolution neural network.

The project is hosted on GitHub Pages and can be accessed at https://raghucharan16.github.io/Hand-Written-Digit-Recognition/.

Features

• Handwritten digit recognition: The system can recognize and classify handwritten digits ranging from 0 to 9.
• Web interface: Users can draw digits on the web canvas provided by the application.
• Real-time prediction: The application provides real-time predictions as the user draws the digit.
• Clear functionality: Users can clear the canvas to start a new drawing.
• Model details: The repository includes the trained model used for digit recognition.

Usage

To use the Hand Written Digit Recognition system, follow these steps:

• Open the web application using the provided link: https://raghucharan16.github.io/Hand-Written-Digit-Recognition/.
• Once the application is loaded, you will see a canvas on which you can draw digits using your mouse or touch input.
• Draw a digit on the canvas. As you draw, the application will display real-time predictions for the digit you are drawing.
• If you want to start a new drawing, click the "Clear" button to clear the canvas and predictions.
• Repeat steps 3-4 as desired.

Development

If you are interested in contributing to this project or running it locally, follow these instructions:

Clone the repository: git clone https://github.com/raghucharan16/Hand-Written-Digit-Recognition.git Navigate to the project directory: cd Hand-Written-Digit-Recognition Open the index.html file in a web browser to access the application locally.

Dependencies

The Hand Written Digit Recognition project relies on the following dependencies:

• HTML5 Canvas: Used for drawing and capturing user input.
• TensorFlow.js: A JavaScript library for machine learning used for the digit recognition model(tensorflowjs works only when tensorflow version <1.6).
• Bootstrap: A CSS framework used for styling and layout.
• jQuery: A JavaScript library used for DOM manipulation and event handling.
• The required dependencies are already included in the project repository, so there is no need for additional setup.

Demo

Contact If you have any questions, suggestions, ideas or issues regarding the project, please feel free to contact the me.

GitHub: Raghucharan16

Your feedback and contributions are most welcome!!