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Create an intelligent license plate recognition system using YOLO + OCR + LLM integration. Detect and read plates with real-time logging and contextual vehicle tracking. Advanced-level. Certificate included.
Startups
Microsoft
NVIDIA
By the end of this course, you'll have the skills to land your first computer vision job or freelance clients.
Learn how AI-powered systems detect and read vehicle license plates automatically.
Use Python with OpenCV and OCR models like Tesseract or YOLO for plate detection and text extraction
Build a real-time system that captures vehicle plates from live CCTV or webcam feeds.
Store detected license numbers with timestamps and vehicle images in a structured database.
Create a GUI to display real-time video, extracted plate numbers, and log history.
Finish the course and receive a verified certificate of success.
Muhammad Yaqoob is the founder of Tentosoft Pvt Ltd and a seasoned Computer Vision expert. With 10+ years of experience and over 5,000+ students taught globally, he brings deep industry knowledge and a passion for practical, hands-on learning.
8 weeks of comprehensive training with 50+ lessons and 10+ hours of content
Real-Time License Plate Detection and Recognition is a computer vision system that automatically identifies and reads vehicle license plates from images or videos. This system enhances vehicle monitoring and traffic enforcement by processing visual inputs in real-time.
Course Course Introduction and Features
Detect license plates live
Works on moving vehicles
Python Development Environment Setup involves installing Python, necessary packages, and configuring tools like Jupyter Notebook or VS Code to enable efficient development, testing, and execution of the license plate recognition system.
Installing Python
VS Code Setup for Python Development
This section provides a comprehensive outline of the License Plate Detection and Recognition System, including the workflow, components, data pipeline, and objectives. It summarizes how the system processes images and identifies plates in real-time.
License Plate Detection and Recognition System Overview
Overview of full system
End-to-end system view
This involves organizing the project structure by managing directories for datasets, models, scripts, and outputs. A clean file structure ensures better maintainability and collaboration during the development of the license plate system.
Understanding Folder and File Structure
ACreate folders for datasets
Organize scripts neatly
Essential packages like OpenCV, TensorFlow, and NumPy are installed and explored to support image processing, model training, and evaluation. This step prepares the environment for building and running the detection system.
Explanation of Required Packages for License Plate Detection and Recognition
Analyzing feedback and identifying patterns
Install key libraries
Enable smooth development
API Access Setup enables external services like vehicle recognition or object detection APIs. This step configures credentials, endpoints, and integration so the system can retrieve results from or send data to third-party services.
Configuring API Key for External Model Access
Analyzing feedback and identifying patterns
Configure API credentials
Connect system to third-party services
This part identifies the critical variables in the codebase, such as input image size, confidence thresholds, and class labels. Understanding these variables helps fine-tune the system for better accuracy and performance.
Customizing Key Variables in License Plate Detection and Recognition
Define input image dimensions
Control Non-Max Suppression parameters
Manage output directory and filenames
This step covers the coding and logic required to detect and recognize license plates. It includes model loading, frame-by-frame analysis, bounding box generation, and character extraction from plates using OCR techniques.
Integrating YOLO Models and Tracking for Vehicle and License Plate Detection
Load the trained detection model
Detect license plate regions using object detection
Display or store recognized plate text
Vision-Language Model Integration enhances detection accuracy by using AI models that understand both images and associated text. These models help interpret plate characters and context more accurately in complex visual environments.
Comprehensive Overview of Vision-Language Model Integration
Combine image and text understanding in one model
Assist in filtering false positives based on context
Leverage pre-trained models for real-world environments
Tkinter is used to create a graphical user interface for the system. It displays the video feed, detection results, and control buttons, enabling users to interact with the license plate recognition system in real-time.
Tkinter Implementation for Real-Time License Plate Detection and Recognition
Create GUI with Tkinter for live video monitoring
Integrate OpenCV video feed into the Tkinter window
Is This Course Right for You?
Kickstart your AI journey with structured, hands-on learning.
Build a portfolio that recuriters can't ignore.
Add powerful AI/CV features to your apps and software.
Upskill for higher-paying, future-ready tech roles.
Build Smarter, more intelligent applications.
Transition into AI even with zero background.
One-time payment for lifetime access to all course materials and updates
Get hands-on experience with real-world projects designed to sharpen your technical skills and build your confidence. Each project is crafted to help you apply concepts practically, write cleaner code, and prepare for real developer challenges.
Highly recommended for small teams who seek to upgrade their time & perform.
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This certificate is given to Jacqueline Thompson for her achievement in the field of education and proves that she is competent in her field.
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