License plate recognition systems can process real-time images and data from a vehicle’s license plates. They can also detect the location and make of a car. They can also match a captured plate with databases of “wanted person,” “protection order,” gang member, National Sex Offender and other sensitive information.
Automatic license plate recognition (ALPR or ANPR) systems rely on specialized hardware cameras and connected software programs to scan vehicle number plates, which can be used for security, surveillance, traffic management and parking control. These systems can quickly identify and match vehicles to a database, making them incredibly useful tools for law enforcement agencies, commercial businesses and government buildings. Throughout the ALPR process, image enhancement and pre-processing techniques, such as noise reduction and contrast adjustment, are utilized to improve the quality of captured images. Edge detection algorithms are also used to identify the characters on a plate, and segmentation and character recognition can then be performed.
The final result is an accurate, high-resolution, ready-to-process image that can be compared with a database of wanted or stolen vehicles. These results can trigger automated actions, such as opening a gate or shortening a driver’s payment process on the way out of an automatic toll road. It helps to speed up and enhance the efficiency of operations and boost worker productivity. The system can even capture images in difficult environments, such as reflected light flare or partially obscured objects.
A crucial tool for police enforcement is an ALPR, or automatic license plate recognition system. It allows officers to quickly identify suspicious vehicles and solve crimes by taking high-resolution photos of license plates from moving vehicles and instantly converting them into alphanumeric characters. These systems use image processing, optical character recognition (OCR), and template matching to recognize vehicle license plates. They also can distinguish between different fonts on the same plate. In addition, they can work under various environmental conditions, from bright sunlight to darkness and fog. Private organizations and businesses can use ALPR to monitor their parking lots, preventing unauthorized vehicles from entering their premises. They can also combine them with other security systems, such as CCTV, to provide full coverage of their facilities. ANPR technology has many benefits but can also be a concern for some people because of its extensive tracking and recording of personal information. However, if this technology is deployed with appropriate data protection measures, it can effectively deter criminal activity and make communities safer. It is especially true when paired with real-time imaging that can be transmitted to police officers.
AI-powered ANPR systems can swiftly and effectively gather and identify license plate photos using sophisticated image processing algorithms. They can convert high-resolution images into alphanumeric characters in seconds, even when the plates are angled or obscured by glare or weather conditions. It enables law enforcement to rapidly match vehicles with databases of stolen or wanted cars and improve traffic management, parking services, and electronic toll collection. It also helps private organizations increase operational efficiency by automating security or customer service processes. For example, a hotel can use LPR to automatically verify a guest’s car and charge the parking fee to their account. However, it is important to note that these technologies are not infallible. Despite the ability of AI-powered systems to process images quickly and accurately, they can still misread a plate, leading to dire consequences for people who were handcuffed at gunpoint and searched by police after their vehicle was mistakenly identified as stolen on an ANPR system. Reviewing the data collection and processing capabilities of any system that uses ANPR technology is crucial.
While technology like license plate recognition systems can significantly improve security, it can also feel invasive and raise privacy concerns. The technology indeed becomes more affordable and easier to use. ANPR systems can be used by private businesses and organizations to keep an eye on vehicles entering and leaving their property. These systems can ensure that only authorized vehicles can access parking lots and controlled entrance points when used with video surveillance cameras.
In addition to capturing license plates, these systems can record vehicle information and GPS coordinates. These records are then compared against local and national databases to identify and locate vehicles, individuals and groups of people. These records are incredibly powerful and can be used for pattern recognition, evidence gathering, forensic analysis and even predictive policing. However, these systems can also be misused by law enforcement to indiscriminately track the movements of millions of ordinary citizens–the vast majority of whom are not suspected of committing any crime. Moreover, these systems are notorious for high error rates, with mistakes sometimes having dire consequences.
Using high-speed cameras, systems that read license plates continuously take pictures of moving vehicles. Smart software then instantly translates them into text and compares them to ‘hotlist’ databases of suspect plates, alerting officers of potential matches. It can help police departments recover stolen vehicles, find missing persons, and investigate traffic infractions and other criminal activity – fast, accurately and effectively. Unlike standard OCR, which can be inaccurate due to different fonts, regions and characters, ALPR uses artificial intelligence to overcome this limitation. It is achieved by employing a multi-layer deep learning model to learn the different variations of license plate fonts, character sets, and regional modifications to achieve high accuracy. ALPR systems are also capable of recognizing multiple plates at a single time. It is achieved by employing a feature detection process consisting of pre-processing, filtering, masking, thresholding and training the CNN, all done at the push of a button. The system can also recognize plates captured at varying angles.