Handwritten Digit Recognition Web App Using CNN | Step-by-Step Tutorial

Handwritten Digit Recognition Web App with CNN | Step-by-Step Tutorial

Description:
In this video, we’ll walk you through the process of building a Handwritten Digit Recognition Web App from scratch! 🚀 Using a Convolutional Neural Network (CNN) model trained on the MNIST dataset, this project recognizes handwritten digits (0-9) drawn by users in real-time. Whether you’re looking to learn more about machine learning, web development, or just want a cool project to show off, this tutorial is perfect for you!

🔹 Project Overview

🔹 What You'll Learn in This Video
- How to train a CNN model on the MNIST dataset for digit recognition.
- How to set up the frontend with HTML, CSS, and JavaScript.
- How the model is loaded in the browser and used for real-time predictions.

🔹 Project Files & Code

🔹 Topics Covered
- Convolutional Neural Networks (CNNs) for image recognition
- Model training and evaluation on MNIST
- Frontend development with HTML, CSS, and JavaScript
- Running machine learning models directly in the browser

🔹 Future Applications of This Project
This project demonstrates the potential of handwritten digit recognition and paves the way for future applications like:
- Mobile handwriting recognition
- Document digitization for financial institutions
- Educational tools for learning to write numbers
- And more!

👨‍💻 Follow Along and Code with Us!
By the end of this tutorial, you'll have a working web app that recognizes handwritten digits and displays confidence scores. Don’t forget to like, subscribe, and hit the bell icon for more machine learning and web development projects!

Links:
📖 Documentation: Check out the README file in the repository for setup instructions and detailed explanations.

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