Have you ever wondered how a character easily zooms in on a number plate in movies? You must have found it quite intriguing, and so did we! It is actually very cool, all because of the help of new advanced technologies. Video security has advanced significantly in recent years, thanks to AI and Machine Learning for powering new capabilities. Number plate recognition is an increasingly in-demand feature in today’s times.
Read till the end to learn about the features, components, and use cases of license plate recognition!
What is Number Plate Recognition (ANPR/ALPR)?
Number plate recognition or License plate recognition, is a technology that can detect and read license plates to make it easier for users or authorities to identify any vehicle on the scene. During incident investigations, authorities review surveillance footage if needed. Most of the time, the number plates are hard to read due to being far away, blurry, or otherwise unreadable. Today’s powerful, more intuitive camera overcomes these hurdles using AI-driven and machine learning technology and analyzes plates in real-time.
This advanced technology goes by several names: Automatic number plate recognition (ANPR), Automatic (or automated) license-plate recognition (ALPR), Automatic (or automated) license-plate reader (ALPR), Automatic vehicle identification (AVI), Car plate recognition (CPR), License-plate recognition (LPR), Mobile license-plate reader (MLPR), Vehicle license-plate recognition (VLPR), and Vehicle recognition identification (VRI). It is a very powerful tool that many users should think of incorporating into their video surveillance system.
How does License Plate Recognition Work?
License or Number Plate Recognition technology captures images of license plates using cameras and then passes the images through several processes, which include Optical Character Recognition (OCR) and image processing. In OCR, special software for ANPR is used to convert the characters on the license plate into alphanumeric codes that can be then compared against a database.
License plate identification consists of seven phases:
1) Image Acquisition: This is the process of an image of a license plate. This can be done using methods like handheld devices and mounting cameras on vehicles or buildings.
2) Pre-processing: This involves preparing images for processing. The images are adjusted to reduce noise and improve contrast.
3) License Plate Localization: LPR systems use algorithms to recognize license plates in the image. It involves recognizing the shape, size, and location of the license plate.
4) Character Segmentation: LPR systems use algorithms to segment characters present in the license plates. This is to ensure that the LPR system can recognize characters accurately.
5) Features Extraction: The algorithms are used to extract features from the image. It involves analyzing the shape, size, and orientation of each character.
6) Character Recognition: The extracted features are compared to a database of known license plate numbers to identify and recognize the license plate.
7) Post-processing: The results are analyzed and appropriate actions are taken based on them. It involves a variety of tasks like sending an alert when the number plate is recognized and storing the information in the database.
Features of AI License Plate Recognition
We know how AI and machine learning have been helpful with technology and making our job easier. They have made license plate identification more accurate and unlocked various benefits. Here are some of the key advantages of AI license plate recognition:
1) Better Accuracy
AI and machine learning processes images more accurately, even when the conditions are not suitable. Their process eliminates the possibility of human error and gives more reliable data. Even at night when the surroundings are dark, AI can easily read the license plates, from different angles, in poor weather. It can also read the plates of vehicles on multiple lanes at the same time.
2) Operational Efficiency
AI helps efficiently operate the LPR systems so no human intervention is needed. For example, in a parking management system, the system guides drivers to available parking spaces and automates payment processes.
3) Scalability
AI technologies help LPR systems to scale more effectively. As the population increases, so does the number of vehicles. Complex scenarios and larger datasets must be managed without compromising performance.
4) Real-time Processing
AI-powered systems are capable of real-time data processing. It is important for settings like traffic control, where reaction times can reduce traffic and boost safety. It enables traffic authorities to adjust traffic signals.
5) Advanced Functionality
This integration of AI provides advanced functionalities, as it improves crime prevention, the algorithms use past data to predict potential security threats and enable predictive analytics. It can combine LPR data with video footage and eliminates the issue of wrong decision-making due to blurry images.
Components of Number Plate Recognition
It is important to understand the components of a number plate scanner in order for it to work properly. Here are the essential components one needs to know:
1) Specialized Cameras: Number plate reader systems depend highly on specialized cameras that are equipped with optical character recognition (OCR) technology. They help capture images of vehicles and their number plates that are high-resolution.
2) Software Algorithms: The images that are captured are processed by powerful software algorithms. They are trained to recognize and isolate license plates in the captured images.
3) Character Recognition: This software identifies the alphanumeric characters on the license plate of a vehicle. This step involves the OCR technology that clarifies the characters captured.
4) Database Integration: Number plate recognition systems integrate with databases containing registered vehicles and information about them. This allows for real-time checks and alerts based on the license plate number.
Technology Used in Number Plate Recognition
Number plate recognition systems apply the Optical Character Recognition (OCR) technology, combined with other image processing techniques that read vehicle number plates. Optical Character Recognition (OCR) is the process that converts an image of text into a readable text format. It means one can convert the text image into a text document with its contents stored as text data. An automated number recognition system is one of the most accurate and widely applied computer vision systems, and the applied methods are constantly evolving to increase the performance, accuracy, cost efficiency, and scalability of license plate reader software.
The ANPR system includes a camera, a processor, and software. The software recognizes and extracts the alphanumeric characters from a captured image of a vehicle’s license plate. The camera captures the image of the license plate. The processor then uses OCR software to identify and extract the characters. The results are then compared against a database of registered vehicle information to verify the vehicle. ANPR technologies utilize OCR alongside additional image processing methods to decipher the characters on vehicle license plates.
In Mobile Systems of Number Plate Recognition
Advances in technology took ANPR systems from being expensive, hard to set up, fixed-based applications to simple “point and shoot” mobile ones. This is possible because the creation of software that ran on cheap PC-based software no longer needed to be given on the pre-defined angles, size, direction, and speed in which the vehicle will be passing the camera’s field of view.
Smaller, cheaper components have led to more law enforcement agencies using advanced technology worldwide. Smaller cameras that can read license plates at high speeds, along with compact and durable processors that fit in police car trunks, allow officers to patrol with real-time license plate recognition, enabling immediate action.
Despite their effectiveness, mobile ANPRs face significant challenges. One major issue is that the processor and cameras need to work fast enough for speeds over 100 mph (160 km/h), which is common with oncoming traffic. The equipment also needs to be very efficient because it relies on the car’s electrical system and must fit in limited space.
Algorithms of Number Plate Recognition Software
License plate scanner software relies on seven primary algorithms to accurately identify license plates. The complexity of each of these steps determines the overall accuracy of the system. Some systems use edge detection techniques to increase the contrast between the letters and the plate backing. Here’s a breakdown of each one:
1) Plate Localization: This algorithm identifies and isolates the license plate within the picture.
2) Plate Orientation and Sizing: This step compensates for any skew in the plate and adjusts its dimensions to the required size.
3) Normalization: The software adjusts the brightness and contrast of the image to enhance visibility.
4) Character Segmentation: This process finds and separates the individual characters on the license plate.
5) Optical Character Recognition (OCR): OCR is used to identify the characters on the plate.
6) Syntactical/Geometrical Analysis: This algorithm checks the characters and their positions against country-specific rules to ensure accuracy.
7) Result Averaging: The software averages the recognized values over multiple images to produce a more reliable result. This is especially useful when a single image might have issues like reflected light flare, partial obstructions, or other visual noise.
Difficulties of Using Number Plate Recognition Software
While using automatic car plate recognition software has significantly been made easier, there are some difficulties users might and do face often. Let’s look at some of the difficulties of using license plate scanner software:
1) Software Licensing
One significant issue with using number plate reader (NPR) software is software licensing. Often, LPR software solutions do not integrate well with existing security or access control platforms, necessitating the purchase of separate software. This complication can make the process more complex and increase costs, frustrating enterprises that are trying to maximize the benefits of their existing hardware.
2) Software Compatibility
The effectiveness of LPR software often depends heavily on the specific camera system hardware or video management system (VMS) being used. If these components are not compatible, the system’s full capabilities cannot be utilized, leading to inefficiencies and unmet expectations. This dependence on compatibility can significantly hinder the implementation and operational efficiency of LPR systems.
3) Managing Additional Hardware
Most LPR cameras require extra equipment, such as on-premise network video recorders (NVRs), which serve as central hubs for device recording storage. This additional requirement not only complicates the installation and operational procedures but also makes them more labor-intensive, time-consuming, and expensive. The need for integrating these components with local servers adds another layer of complexity to the process.
4) Unforeseen Costs
Deploying LPR systems can involve hidden fees and unexpected expenses, such as charges for additional hardware or fees based on the number of cameras and monthly lookups. These extra costs can strain budgets and complicate financial planning for enterprises investing in LPR technology.
5) Limited User Seats
User access limitations can also be a source of frustration. Some providers restrict the number of users who can access the system per license. While this might not be an issue for small to medium-sized businesses, larger enterprises can quickly exhaust these user limits, leading to operational inefficiencies and potential bottlenecks in accessing the system
6) Data Retention
Longer retention periods are generally preferred for comprehensive data analysis, but some providers offer only shorter durations by default or charge extra for extended retention. This limitation can be problematic for organizations that rely on long-term data storage for their operations, necessitating careful consideration of data retention policies when selecting an LPR solution.
OpenCV: Automatic License/Number Plate Recognition (ANPR) with Python
OpenCV-based Automatic License/Number Plate Recognition (ANPR) with Python is a system designed to automatically find and read license plates from photos or videos. OpenCV is a free library that provides tools for image processing, making it easier for computers to analyze and understand visual data. Python is a popular programming language known for its simplicity and ease of use, which helps developers quickly write and test code.
When combined, OpenCV and Python create a powerful system for ANPR. The process starts by capturing images or video frames that contain vehicles and their license plates. These images are then processed to improve their quality, making it easier to identify the license plates. The system detects the area of the image where the license plate is located, isolates it, and then uses Optical Character Recognition (OCR) to read the characters on the plate. This information can be used for various applications such as monitoring traffic, managing parking lots, enforcing traffic rules, and automating toll collection.
Use Cases of Number Plate Recognition
A license plate reader is used in a wide range of settings. Any area visible in the field of view of the camera can benefit from the LPR. A number plate scanner is used in various locations, such as Parking Lots & Garages, Entry Stations & Gates, drive-thru, Toll Gates & Booths, and Car Washes. Now that we know where NPR is usually implemented, let’s learn how NPR has proved to be beneficial to these locations:
1) Securing a Restricted Area
ANPR systems play an important role in securing restricted areas by verifying vehicles entering and exiting. ANPR ensures that only permitted vehicles gain access. This reduces the risk of unauthorized entry, enhances perimeter security, and allows security personnel to focus on other critical tasks rather than manual checks.
2) Monitoring Gate Entrance
ANPR enhances the effectiveness of gate monitoring by swiftly capturing and recording vehicle license plates. This technology automates the identification process, minimizing delays and queues at entry points.
3) Resolving Accidents
In the event of accidents, ANPR systems contribute significantly to resolving incidents swiftly and efficiently. By capturing and recording license plate data at the scene, ANPR helps authorities quickly identify and contact vehicle owners or insurers. ANPR data also serves as valuable evidence in accident investigations.
4) Tracking a Vehicle
NPR technology is invaluable for tracking vehicles in various applications, such as logistics management, fleet tracking, and law enforcement operations. By continuously monitoring and updating vehicle locations in real-time, ANPR enables businesses to optimize fleet operations, improve route planning, and enhance overall operational efficiency.
5) Finding a Vehicle
ANPR systems assist in locating vehicles by automatically identifying vehicles of interest, such as stolen vehicles or those involved in criminal activities. When a vehicle passes through ANPR-monitored areas, the system can alert authorities, enabling swift response and recovery efforts.
6) Traffic Management
ANPR plays a crucial role in traffic management by providing real-time data on vehicle movements and patterns. By analyzing traffic flow and congestion hotspots, ANPR helps transportation authorities optimize traffic signals, adjust lane configurations, and implement effective traffic management strategies.
7) Law Enforcement and Security
ANPR enables law enforcement agencies to quickly locate and apprehend suspects, recover stolen property, and prevent crimes. This proactive approach to law enforcement improves public safety and enhances community policing efforts
8) Emergency Services
ANPR systems support emergency services by providing critical information during emergencies and accidents. By automatically detecting and reporting incidents, such as accidents or medical emergencies, ANPR helps emergency responders prioritize and dispatch resources accordingly.
How is License Plate Recognition helping us?
License Plate Recognition (LPR) systems, also known as Automatic License Plate Recognition (ALPR), have become increasingly important due to their numerous benefits:
1) Increased Security
LPR systems enhance security by ensuring that only authorized vehicles can access restricted areas and parking lots. By automatically recognizing license plates against a database of approved vehicles, they prevent unauthorized entry and bolster overall security measures.
2) Improved Efficiency
These systems automate the process of identifying license plates, which speeds up access control and reduces wait times. This efficiency saves both time and money, making operations smoother and more cost-effective.
3) Reduced Costs
LPR systems help cut down on expenses associated with manual license plate checks and enforcement, such as labor costs for security personnel and issuing parking tickets. This automated approach streamlines operations and reduces administrative overhead.
4) Improved Accuracy
By automating license plate recognition, LPR systems minimize human error, ensuring more accurate identification of vehicles. This reduces the risk of mistakes in access control, enforcement actions, and tracking of vehicles involved in incidents.
5) Increased Safety
LPR systems contribute to road safety by identifying vehicles linked to accidents or other incidents. Law enforcement can use this information to quickly respond to emergencies, manage traffic incidents, and enhance overall public safety on the roads.
Conclusion
Number plate recognition technology is revolutionizing the way we manage and monitor vehicles on our roads. This digital transformation in automotive industry is making our lives easier and our communities safer. As technology continues to advance, we can look forward to even more innovative applications that will keep our roads and cities running smoothly. So, the next time you drive past one of those roadside cameras, remember that they are part of a smarter, safer for everyone!
