Project Overview
A machine learning project that leverages deep learning and computer vision to automatically detect and read characters from license plates in images and videos.
Technologies Used
- Python
- YOLOv8
- EasyOCR
- OpenCV
- PyTorch
- Matplotlib
Key Features
- High-accuracy object detection with a trained YOLOv8 model.
- Optical Character Recognition (OCR) to extract plate text.
- Advanced image preprocessing pipeline with OpenCV.
- Real-time detection capabilities on video streams.
The Challenge
Manually identifying and logging license plate information from images or video footage is slow, labor-intensive, and prone to human error. An automated system must be robust enough to handle various real-world conditions like different angles, lighting, and plate formats.
My Solution
I built a complete pipeline in Python to solve this. First, a YOLOv8 (You Only Look Once) object detection model, which I trained on a custom dataset, identifies the location of license plates in each frame. The detected plate region is then passed to EasyOCR, a powerful optical character recognition library, which extracts the alphanumeric characters. The entire process is optimized for accuracy and speed.
Core Idea
Combine a state-of-the-art object detector with a specialized text reader to create a fast and accurate license plate recognition system.