Automatic license plate recognition (ALPR), which has a variety of uses from traffic enforcement to locating stolen automobiles, is the present, not the future, of technological advancement.
The US market for license plate recognition technology will expand significantly, as is expected on a global scale. By 2023, it is anticipated to reach $3.57 billion.
Having an ALPR solution
Having an ALPR solution that crafted for your company implies integrating technology with conventional methods. The tech-based strategy can give you enhanced data processing abilities, strategies for streamlining repetitive processes, and cutting-edge solutions to speed up output and improve workflows.
The popularity of License Plate Recognition Software has increased recently as a result of its many advantages for a variety of applications. A few benefits of ALPR include journey time analysis, intelligent parking, automated tolls, intelligent transportation systems in smart cities, and traffic management.
What is the process behind license plate recognition? Continue reading today’s article to find out how it works.
What Is ALPR?
Automatic license Plate Recognition (ALPR) is a technique that reads a vehicle’s registration number from photographs of its license plate using optical character recognition. Different image processing techniques applied by an automatic license plate recognition system to swiftly and automatically identify vehicles in video or photo footage from cameras.
- Automatic License Plate Recognition Software
Numerous uses for an automatic license plate recognition system include tracking the location of moving vehicles, recognizing specific automobiles, automating parking enforcement, and more. As technology develops quickly with the introduction of machine learning and deep learning, the cost of computation falls, and the accuracy of used image processing techniques rises, the usage of ALPR systems is becoming more widespread.
How Does ALPR System Works
ALPR (automatic license plate recognition) systems read license plates by combining optical character recognition (OCR) with additional image processing methods.
The approaches used to improve the performance, accuracy, cost-efficiency, robustness, and scalability of automatic license plate recognition software are constantly changing. ALPR is one of the most precise and widely used computer vision systems.
- ALPR procedures
Popular ALPR methods include a few different approaches that are combine as a part of the computer vision pipeline. The methods necessary to convert video or picture input into insightful, complete information contained in the vision pipeline. These are the elements that are most crucial:
- Rapid Object Detection
To identify vehicles and other vehicle classes (bus, truck, car, van, motorcycle, etc.) in photos and video streams, object identification uses deep learning. Modern object detection algorithms like YOLOv3 and YOLOv7 make use of neural networks that have been honed using a collection of photos.
- Optical Character Recognition (OCR)
A crucial computer vision method for extracting text from images is optical character recognition (OCR). As a result, license plates recognized by the ALPR system. To identify individual characters, confirm their order, and translate the number plate image into text, AI image processing services like Tesseract OCR or MaskOCR algorithms are utilized.
- Templet Matching
In order to identify a match, a technique called template matching compares a license plate image to a library of license plate images. A method for identifying a particular pattern in a text string is template matching. The set of characters in the ALPR scenario is the license plates of the vehicles.
ALPR Method
A digital image capture device (camera), a processing unit, and several video analytics algorithms are components of typical ALPR systems.
- Image Acquisition Through Video Input
First, the ALPR camera records pictures or videos with one or more license plates on them (video stream or photo). Infrared lighting frequently utilized to enable cameras to record vehicle license plates at night, enabling the use of ALPR at all times of the day.
- Cropping And Detecting License Plate
The license plate identified in the image using machine learning and computer vision techniques. There are various approaches, and they vary widely in terms of complexity, speed, and accuracy of computation. A typical technique starts with object identification to identify the cars, followed by localization of the numberplate within those bounding boxes. Finding areas of contrast between the background and the numberplate is often how to do this. After being identified, the license plate is cropped and adjusted (sharpened, distorted and enhanced).
- Reading By Extracting The License Plate
The license plate number then returned in text format once OCR software applied to the detected plate area. The OCR software enables the usage of the same ALPR equipment across nations optimized for various character sets. The license plate number, frequently along with the area or country identification, is the standard output of an ALPR system.
- Utilize License Plate Information
The car plate number is then kept in a registry for integration with some other IT systems after being converted to plain text format. The license plate compared to a registry of registered plates, as well as whitelist and blacklist registries. The software returns saved data about the car, such as the name and address of the registered owner, if it is present in the database.
Benefits Of Using Automatic License Plates Recognition
Numerous benefits of ALPR serve as the foundation for practical applications. Automation of manual chores, extremely effective space management, improved governance, and improved customer experience are the main advantages of ALPR.
- Automation
Number plate recognition software enables automatic facility alarms and controls. ALPR is therefore a crucial technology for smart cities or toll booths. Accuracy: ALPR systems can rapidly and simply detect vehicles by their license plates and can attain very high accuracy.
- Accuracy
ALPR systems can detect automobiles quickly and easily by their licence plates and can attain very high accuracy.
- Analytics
Analytics of traffic flow can be performe using the generate data. This is crucial when running Intelligent Transportation Systems (ITS), which employ data processing technologies to enhance the mobility of people and commodities, control demand, boost safety, lessen traffic congestion, and effectively handle incidents.
- Convenience
In order to give end users a seamless and trouble-free experience, ALPR typically connected with other IT systems and runs in an ecosystem. In order to improve customer experience and offer new services and as goods, such as automated parking payments, technology deployed.
Ecological Effects
By reducing needless driving, ALPR systems can help reduce traffic congestion and pollution. Reducing wait times and time spent looking for parking in cities contributes to a reduction in pollution.
- Versatility
Automated vehicle identification has several uses, including parking management, security, traffic enforcement, manufacturing logistics optimization, and more.
- Security
For many surveillance and security applications of computer vision, ALPR systems are very important. By offering a way to independently identify and track many cars, such systems contribute to increased security.
- Identification
The foundation for quick and seamless vehicle identification is quick number plate recognition. The identifier used to locate and track particular cars or to grant them entrance.
- Cost-Efficiency
There is no need for human input in accurate and quick number plate recognition. Since a large number of license plates evaluated extremely fast. This approach to identifying automobiles is very effective. As a result, it promotes cost-effective governance and shortens wait times.
- Minimal Footprint
Installing and running an automatic number plate recognition system is comparatively inexpensive. An array of cameras used for ALPR.
- Scalability
The creation of distributed systems is made possible by the usage of cutting-edge technologies like IoT (or AIoT) and Edge AI (machine learning at the edge). Implementing offline capabilities, private video analytics, and cost-effective video analytics are all made possible by edge computing.
Conclusion
High-quality cameras that adapt to various environmental conditions required for automatic license plate recognition or number plate recognition. Cameras should be safely mount so they can observe the roadways and the surrounding area, either on a moving object or at a stationary point.
Cameras will be able to read license plates day or night, in excellent or bad visibility conditions, and in any weather with the help of top-notch client-oriented ALPR software. When an image is ready to be utilized for optical character recognition, then in this case the software works with preprocessed versions of the image. These systems can successfully handle all kinds of solutions as well.