How to do Automatic Number Plate Recognition using Ultralytics YOLO11 and OpenAI GPT-4o-Mini Model 🎉

Video Description

In this tutorial, we will explore how to perform Automatic Number Plate Recognition (ANPR) using Ultralytics YOLO11 and the GPT-4o-mini model from OpenAI. This step-by-step walkthrough demonstrates how to set up your environment, process video frames, and use prompt engineering with GPT for highly accurate number plate text recognition. You’ll also learn how to combine detection and generative AI tools for an advanced, real-time ANPR solution. Key highlights: 00:00 - Introduction to the ANPR workflow using YOLO11 and GPT-4o-mini. 01:07 - A quick walkthrough of the Google Colab Notebook setup. 01:44 - Why YOLO11 and GPT-4o-mini are ideal for number plate recognition. 02:10 - Installation and import of essential dependencies, including Ultralytics and OpenAI. 02:27 - Downloading the sample video and YOLO model from Ultralytics assets. 03:29 - Reading the video file and initializing the number plate detection model. 04:41 - Configuring the OpenAI client for accurate number plate text extraction. 05:12 - Crafting effective prompts to enhance GPT-4o-mini results. 06:31 - Frame-by-frame video processing and applying padding for improved recognition accuracy. 10:27 - Conclusion and Summary 🔗 Colab notebook ➡️ https://github.com/ultralytics/notebooks/blob/main/notebooks/how-to-use-ultralytics-yolo-with-openai-for-number-plate-recognition.ipynb 🔗 Key Ultralytics Resources: 🏢 About Us: https://ultralytics.com/about 💼 Join Our Team: https://ultralytics.com/work 📞 Contact Us: https://ultralytics.com/contact 💬 Discord Community: https://discord.com/invite/ultralytics 📄 Ultralytics License: https://ultralytics.com/license #anpr #ultralytics #yolo #computervision #objectdetection #machinelearning #deeplearning