How ANPR technology is revolutionizing vehicle identification – Part 1 – What is ANPR?
Automatic number plate recognition (ANPR), also known as License plate recognition (LPR) technology has gained significant popularity in recent years due to its many benefits across various applications.
It plays a crucial role in traffic management, intelligent parking, toll automation, enhancing transportation systems in smart cities, and facilitating journey time analysis. In the first part of this article, we'll delve into practical insights about ANPR, including what it is, how it works to identify vehicles, and the advantages of automated plate recognition.
What is ANPR?
Automatic Number Plate Recognition (ANPR) technology employs optical character recognition (OCR) to analyze images of vehicle registration plates and extract the registration number. This system utilizes various image processing techniques to swiftly and autonomously identify vehicles from images or live video captured by one or multiple cameras.
An ANPR system serves multiple purposes, including vehicle tracking, car identification, automated parking enforcement, and more. The popularity of ANPR systems is on the rise, fueled by rapid technological advancements in machine learning and deep learning, reduced computational costs, and improved accuracy in image processing techniques.
ANPR system types
There are two main ANPR system types:
Fixed Systems - These systems are permanently installed at specific locations. They operate continuously and capture license plate information from vehicles passing through those points.
Use Cases:
?Mobile Systems - Mobile ANPR systems are portable and mounted on vehicles (such as police cars). They can move around and scan license plates while on the move.
Use Cases:
How does ANPR work?
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ANPR systems are sophisticated technologies designed to automatically capture and interpret license plate information from vehicles. These systems combine optical character recognition (OCR) with various image processing techniques, enabling them to read and analyze license plates efficiently.
Some of its key components include:
1) Real-time object detection
Deep learning-based object detection algorithms are utilized to identify vehicles and classify them into different classes such as cars, buses, trucks, etc., from images or videos.
2) Image processing Traditional computer vision methods are employed to preprocess images and enhance their quality for OCR algorithms. Given the challenging real-world conditions ANPR systems often encounter, such as varying lighting, and weather, image processing functions such as sharpening, color correction, and cropping are applied to improve subsequent algorithmic outputs significantly.
3) Optical character recognition (OCR) OCR techniques are crucial for extracting text from images, enabling ANPR systems to recognize license plates. Algorithms are employed to detect individual characters, validate their sequence, and convert the license plate image into text.
4) Template matching This involves comparing a license plate image against a database of plate images to find a match. It functions by identifying specific patterns within text strings, with the set of characters representing vehicle number plates in the context of ANPR.
These techniques work in tandem to enable efficient and accurate recognition of license plates, facilitating various applications ranging from law enforcement to parking management.
Want to test an ANPR system?
PLATE.VISION - License Plate Identifier by Makewise
The PLATE.VISION system - ANPR (Automatic Number Plate Recognition)? - is a real-time vehicle identification software (License Plate, Vehicle Identification Number, Brand and color, etc.).
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