Handwritten Digit Recognition using Python

Summary:

This Python project focuses on implementing a Handwritten Digit Recognition application using the MNIST dataset. Handwritten digit recognition involves teaching computers to recognize human-written digits, addressing the challenge of variations in writing styles. The project utilizes Convolutional Neural Networks (CNNs) and includes the development of a graphical user interface (GUI) to draw digits for immediate recognition.

About the Project:

The project employs the popular MNIST dataset, consisting of 60,000 training images and 10,000 testing images of handwritten digits from zero to nine. The images are represented as a 28x28 matrix of grayscale pixel values. The goal is to train a deep neural network using Python, Keras, and the Tkinter library to recognize handwritten digits accurately.

Implementation Steps:

Import Libraries and Load Dataset:

Import required modules, including Keras for deep learning. Load the MNIST dataset using mnist.load_data().

Preprocess the Data:

Reshape the image data to add a dimension suitable for CNN input. Normalize and preprocess the data for neural network training.

Create the Model:

Develop a CNN model with convolutional and pooling layers. Compile the model using the Adadelta optimizer. Train the Model:

Use the model.fit() function to train the CNN model. Save the trained model and weights in the 'mnist.h5' file. Evaluate the Model:

Assess the model's performance on the test dataset. Display test loss and accuracy.

Create GUI for Digit Prediction:

Develop a GUI application using Tkinter to draw digits on a canvas. Implement a prediction function using the trained model to recognize drawn digits. GUI Digit Recognizer: The project includes a separate Python file (gui_digit_recognizer.py) for the GUI:

Load the trained model using load_model from Keras. Create a Tkinter-based GUI with a canvas for drawing digits. Implement functionality to recognize drawn digits and display results.

Conclusion:

This project successfully combines deep learning techniques with GUI development to create an interactive Handwritten Digit Recognition application. The Convolutional Neural Network proves effective for image classification, and the Tkinter-based GUI enhances user experience by allowing digit drawing for real-time recognition.