Handwritten Digit Recognition Project Readme

Overview

This Python project implements a Handwritten Digit Recognition app using Convolutional Neural Networks (CNN) and the MNIST dataset. The app includes a graphical user interface (GUI) for drawing digits and getting instant predictions.

Prerequisites

• Basic Python knowledge
• Understanding of deep learning with Keras
• Familiarity with Tkinter for GUI

Steps to Implement

1. Load and Preprocess Data:

   • Import necessary libraries.
   • Load the MNIST dataset and preprocess the data.

2. Create CNN Model:

   • Build a simple CNN model using Keras.

3. Train the Model:

   • Train the model with the MNIST dataset.
   • Save the trained model.

4. Evaluate the Model:

   • Check the model's accuracy on the test dataset.

5. Create GUI for Digit Recognition:

   • Implement a Tkinter-based GUI.
   • Allow users to draw digits.
   • Utilize the trained model to predict the drawn digit.

Usage

1. Train the Model:

   python train_model.py

2. Run the GUI:

   python gui_digit_recognizer.py

   Brief This Python project leverages Convolutional Neural Networks (CNN) and the MNIST dataset to create a Handwritten Digit Recognition app. The model is trained to recognize digits from zero to nine. Users can draw digits on a Tkinter-based graphical user interface (GUI), and the trained model instantly predicts the drawn digit. The project focuses on simplicity, making it accessible for those with basic Python knowledge. It serves as a practical example of applying deep learning concepts, offering a hands-on experience in training models and integrating them into interactive applications. The GUI provides a user-friendly platform to explore the capabilities of digit recognition using machine learning.

Conclusion

This project showcases a simple Handwritten Digit Recognition system with an interactive GUI. Explore and modify the project to suit your needs.