Mastering YOLO: Build an Automatic Number Plate Recognition System


In this comprehensive guide, you'll learn everything you need to know to master YOLO. With detailed explanations, practical examples, and step-by-step tutorials, this book will help you build your understanding of YOLO from the ground up.
Discover how to train the YOLO model to accurately detect and recognize license plates in images and real-time videos.
From data collection to deployment, master every step of building an end-to-end ANPR system with YOLO.
Here's what you'll get with this book:
  • Source code used in the book.
  • Hands-on coding experience and real-world implementation.
  • A step-by-step guide with clear explanations and code examples.
  • Gain practical skills that can be applied to real-world projects.

Let me show you what is covered inside this Mastering YOLO Book

Set Up Your Environment for Object Detection

Training an object detection model on a CPU will take hours if not days, so using a GPU is essential. With my step-by-step guide, I'll walk you through the installation of Miniconda, CUDA, and cuDNN. You'll be up and running in no time.
Believe me, I know how frustrating it can be to get stuck on the environment setup. That's why I'm here to help you get through it smoothly. And once we're done with this step, we can move on to the exciting stuff - gathering and labeling the data, and training your very own YOLO model!
Are you ready? 

Collect the Data for Training the Model

Data is the backbone of any machine learning model, and object detection is no exception. But gathering and labeling data can be a daunting and time-consuming task, especially if you're new to the field. In this book, I'll guide you through the entire data preparation process step-by-step, with practical tips and tricks to help you avoid common pitfalls and roadblocks.
You'll learn the art of data collection, discover a powerful tool for labeling your images and annotating your datasets and learn how to split your data for optimal performance. And the best part? You'll see it all in action, with real-world examples of how to prep your data for number plate detection.

Train the YOLO Model and Learn How to Use it to Detect Number Plates in Images and Video Streams

Training a YOLO model may seem daunting at first, but with the guidance provided in the book, it's not as difficult as it seems. 
Trust me, I've been where you are. When I first started working with YOLO, the process seemed complex and challenging, with many moving parts to keep track of. But after putting in the time and effort to understand the intricacies of the model, I can confidently say that it's simpler than you might think.
By following the detailed instructions in the book, you'll be able to train your own object detection model with ease, even if you have little to no experience with deep learning. By the time you're finished, you'll be amazed at how quickly you can train a high-performing object detection model.
Once the model is trained, you'll learn how to use it to detect number plates in images and video streams.

Learn How to Recognize Number Plates in Images and Videos Using OCR

In this section of the book, I'll show you how to apply OCR to extract text from number plates in images and videos. We'll use the EasyOCR package to get you up and running quickly, so you can extract text and get a working prototype.
However, it's important to be aware of some issues that can arise when applying  OCR. If you want to find out what are these challenges and what you can do to overcome them, then join me inside the book!

Integrating the Number Plate Recognition System with a Streamlit Web Application

Looking to take your number plate recognition system to the next level? Let me introduce you to the world of web applications! In this section of the book, we'll be creating a fully functional web application using Streamlit, a popular Python framework for creating data-driven applications.
After a quick overview of the Streamlit framework and its benefits, we'll dive right in and start building our own custom web application. First, we'll integrate our trained number plate detection model into the app, allowing us to detect number plates in images.
Next, we'll use Optical Character Recognition (OCR) to recognize the characters on the number plates, and we'll display the results directly on our web application. We'll also add some extra features to make our app more user-friendly, such as the ability to upload images and a spinner feature to give users visual feedback while the number plate recognition system processes the uploaded images.

What People are Saying

Wael Youssef - PhD Computer Science, Founder, Leading research in Computer Vision & ML

This e-book is a valuable resource for those interested in building a license plate recognition system using YOLO. The step-by-step instructions and clear explanations make it accessible even to those with limited experience in the field. The practical applications of the system make it a valuable addition to any developer's toolkit.



Who Is This Book For?

This book is aimed at individuals who already have some basic knowledge of Python programming, OpenCV, and computer vision
It is ideal for Python programmers who are looking for a practical, hands-on guide to building more advanced object detection and recognition projects. 
It is also suitable for anyone familiar with OpenCV and computer vision who wants to take their skills to the next level and learn how to apply object detection to solve real-world problems. 
Whether you're a hobbyist, a student, or a professional developer, this book will provide you with the knowledge and tools you need to get started with building your own object detection and recognition systems.

Table of Contents

1. What is Object Detection

2. Advancements in Object Detection

3. YOLO: The Object Detection Framework

    3.1. What is YOLO
    3.2. How YOLO works
    3.3. YOLO Architecture
    3.4. YOLO Versions

4. Environment Setup

    4.1. Install Miniconda
    4.2. Install the Required Packages
    4.3. Install CUDA and cuDNN for GPU support
    4.4. Project Structure

5. Data Preparation

    5.1. Gathering the Data
    5.2. Labeling the Data
    5.3. Splitting the Data
    5.4. Creating the YAML File 

6. Training the YOLO Model

    6.1. Choose a Model
    6.2. Start Training

7. Detecting Number Plates with the Trained Model

    7.1. Number Plate Detection in Images
    7.2. Number Plate Detection in Videos

8. Recognizing Number Plates Using OCR

    8.1. Number Plate Recognition in Images
    8.2. Number Plate Recognition in Videos

9. Create a Web Application with Streamlit

    9.1. Introduction
    9.2. Installing Streamlit
    9.3. Creating a New Streamlit App
    9.4. Adding Upload Feature
    9.5. Integrating our Number Plate Recognition System with Streamlit

10. Conclusion

About the Author

Hi, My name is Yacine Rouizi and as a self-taught Python programmer, I have a passion for creating content about machine learning, deep learning, and computer vision.
I hold a level 1 Master's degree in Physics, and I am originally from Algeria. I am thrilled to have the opportunity to share my passion for technology and programming with others.

My journey in programming began in 2019 and since then, I have been constantly learning and improving my skills.
I help thousands of people each month through my blog, , and I've created 3 courses (including this one) that have reached more than 1,000 students. With more than 50 published articles  covering web development, machine learning, deep learning, and computer vision, I'm committed to sharing my knowledge and helping others succeed in these exciting fields.

I believe that through clear explanations and practical examples, I can help make these complex topics
accessible to everyone.


(One-time payment)

  • Digital book (PDF format)
  • Source code, example images, and videos used in the course
  • Lifetime access to future revisions of the book
  • 14-day money-back guarantee

100% Money Back Guarantee

I understand that trying something new can be a bit intimidating. That's why I offer a 100% Money Back Guarantee. If for any reason you are not completely satisfied with your purchase, simply let me know within 14 days of buying and I'll give you a full refund. No questions asked.
I am confident that my book will meet your expectations, and I want to make sure that you are completely satisfied with your purchase. With my Money Back Guarantee, you can feel confident that your investment is safe, and you have nothing to lose.

Frequently Asked Questions

Who is this book for?

This book is for Python programmers who want to build more advanced projects in object detection and recognition systems. It's also suitable for anyone familiar with OpenCV and computer vision.

What kind of support can I expect after purchasing the book?

I am committed to your success, and I offer email support to answer any questions or concerns you may have.

What if I'm not satisfied with the book?

I am confident that you will find this book valuable and informative. However, if for any reason you are not satisfied with the book, I offer a 30-day money-back guarantee. Simply contact me and I will provide a full refund ASAP.

 Is this book suitable for beginners?

While some knowledge of Python, OpenCV, and computer vision is recommended, this book is written with clear and concise instructions that are easy to follow. Even if you are new to these topics, you can still benefit from this book.

How long does it take to complete the book?

The book is designed to be completed at your own pace. However, if you follow the step-by-step instructions provided in each chapter, you should be able to complete the book in a week or so.

I have another question.

I am here to help! Please don't hesitate to reach out to me at with any additional questions or concerns you may have.

   Last Updated: apr 2024