Welcome to the Handwritten Digit Classifier project! This application leverages the power of machine learning to classify handwritten digits, using a Django web framework for the backend. The goal is to provide a user-friendly interface where users can draw a digit on the screen, and the application will predict the digit based on a pre-trained machine learning model.
Interactive Interface: Users can draw digits directly on a canvas and submit them for classification. Real-Time Predictions: The application delivers instant predictions based on the drawn digit. Robust Machine Learning Model: The digit classification is powered by a pre-trained machine learning model, ensuring high accuracy. Django Backend: Utilizes the Django framework to handle the backend operations, providing a scalable and secure foundation.
Frontend: The user interface is built using HTML, CSS,React and JavaScript, offering an intuitive drawing experience. Backend: Django serves as the backbone of the application, managing user interactions and communicating with the machine learning model. Machine Learning: The classification model is trained on the MNIST dataset, a well-known dataset of handwritten digits, ensuring reliable predictions.
The MNIST dataset is publicly available and was used for training the machine learning model. Special thanks to the Django and TensorFlow communities for their excellent resources and documentation. Special thanks to Pyplane for django tutorial
Project from Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition