Ultralytics YOLO11 | Training, Inference, Benchmarking, and Deployment Explained! 🚀 - How to Build an Automatic Number Plate Recognition System using EasyOCR & Ultralytics YOLO11 🚀 🤯

Recognizing vehicle number plates automatically is one of the most practical applications of computer vision and optical character recognition (OCR). In this tutorial, we’ll build an automatic number plate recognition (ANPR) system in Python using EasyOCR, a popular open-source text detection library, and Ultralytics YOLO11 model. We’ll start with the basics of ANPR, discuss its real-world applications in traffic monitoring, parking management, and toll collection, and then explore the EasyOCR GitHub repository. You’ll see a demo video, followed by a complete, step-by-step Python code walkthrough showing how to detect vehicle number plates with YOLO11 and extract text from them using EasyOCR. By the end, you’ll understand how to combine object detection and OCR techniques to develop your own ANPR system, one that can be integrated into real-world smart city or transportation solutions. Chapters: 00:00 - Introduction to automatic number plate recognition (ANPR) 00:22 - Applications of automatic number plate recognition 00:42 - Introduction to EasyOCR for text extraction 02:00 - EasyOCR GitHub repository walkthrough 03:31 - ANPR demo video 04:21 - Step-by-step ANPR code walkthrough in Python 08:01 - Running the ANPR code and visualizing results 09:58 - Conclusion and key takeaways 🔗 Code ➡️ https://drive.google.com/file/d/1jKafIsLdIRVI94QmZeFlOq3aKoPhSufl/view?usp=sharing 🔗 Number plate detection model ➡️ https://drive.google.com/file/d/1C43R0SXR8GqnJAKDG15ggOr7U7MBjw3F/view?usp=sharing Ultralytics YOLO Resources: 💻 GitHub Repository: https://github.com/ultralytics/ 📚 Documentation: https://docs.ultralytics.com/ #anpr #easyocr #objectdetection #computervision #ultralytics #python