![]() ![]() For recognizing an image by a camera, the computer vision technique is responsible. There is a difference between license plate image capture and image recognition. OCR process consists of six algorithms that, in combination, ensure accurate output. A top-notch software able to handle specific fonts, colors, two or more rows, and blurred images is a great way to speed up the work of human operators since the algorithm can complete operations many times more efficient. It depends on a quality image database where multiple images are gathered, which allows the algorithm to find a match between the two images. OCR is a powerful yet complex technology. And optical character recognition (OCR) algorithms underpin custom ALPR solutions. ALPR systems provide enough data from model training. ML models, including those fueling license plate recognition technology, require huge amounts of data. The analytic software translates still images and videos into machine-readable characters. Computer Vision and OCR for License Plate Recognition Technology The period of data storage in the ALPR database is normally five years. The ALPR data recorded by cameras is not private and does not provide any personal information to any third party. Thus, they have multi-purpose applications, involving traffic and highway management, crime prevention and tracking, detection of stolen vehicles, recovery of stolen or lost license plates, and more. ![]() Cameras can record at any time of the day and any weather conditions. For instance, they can capture license plates, time, and date when the car is parked, or they can help the police monitor city areas were citizens are alarmed. Mobile ALPR cameras can be often seen attached to police patrol cars. This video can be instantly uploaded to a central server so that to keep all the information in one place, available for later viewing. On top of that, ALPR cameras are capable of getting a vehicle on a short video. ![]() The best solution to this problem is to have multiple ALPR cameras in different locations that all record at the same time. With only one camera capturing the data at a time, a driver may easily pass by a traffic camera unnoticed, and a camera won’t capture any accident. That can be especially tough when monitoring a road that has two or more lanes with opposing traffic in each of them. Stationary ALPR cameras, being placed at fixed locations, can record all the cars entering a particular area of the roadway. This camera can be placed on a stationary object or mounted on the vehicle. A camera captures the license plate number of a stopped or moving vehicle and helps identify a suspect vehicle, for instance. The data can then be used for a variety of purposes: security and surveillance, traffic management, toll collection, parking control, or vehicle location on the map.Īn important part of an ALPR system is a camera. And analytic software is then needed to classify the number plates according to each state’s license plate code (for example, in the case of Massachusetts, the code will be M). ALPR Rests on Hardware and Software ComponentsĪLPR allows scanning the plates from all registered vehicles in a given state. So, how does license plate recognition work? Read on to learn how it goes and where to apply custom ALPR solutions for better returns. The tech-based approach can grant you advanced data processing capabilities, methods to optimize routine tasks, and state-of-the-art tools to expedite output and add value to workflows. It is projected to reach $3.57 billion by the year 2023.įor your business, owning an individually tailored ALPR solution means the injection of technology in traditional approaches. The technology has gone through improvements year by year until machine learning (ML) and deep learning (DL) have uncovered new ways for progress.Īs it is predicted globally for license plate recognition technology, the US market will see significant growth. Full-Cycle Web Application Development for a Retail CompanyĪutomatic license plate recognition (ALPR) is the present, not the future of technology development, that has a wide array of applications, from traffic enforcement to tracking stolen vehicles down. ![]()
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