License plate recognition, sometimes abbreviated to LPR, is a feature on some software that can detect, read, and store video of a vehicle’s license plate. Used fundamentally in traffic monitoring in parking lots and gated security entrances, the camera can capture a license plate number compared to a database.
License plate recognition systems provide more innovative vehicle surveillance solutions. It is basically using for one of the prime concerns of the security sectors since automated monitoring saves a lot of their time. Which, therefore, allows them to focus on other essential tasks that need their attention.
So, here are some of the top reasons which make usage of Automated License Plate Detection systems a must!
The AI-based License Plate or Number Plate Recognition (ALPR/ANPR) systems provide immediate benefits for law enforcement, parking agencies, security teams, government buildings, gated communities entrances, and more. While License Plate Recognition systems are using in many different cases, it’s proven to effectively deter crime, analyze and collect data, and provide agencies with real-time solutions.
Situations that use license plate capture may be particular, but this feature is essential if you need to:
A successful application of license plate recognition is to automate access control for parking structures. A camera is located at the entrance gate. It will automatically record a coming vehicle and detect a license plate to compare it to an authorized visitor’s vehicle’s license plate numbers database. If that license plate number is available in the database, the gate will open and permit the vehicle to enter.
Like all other technologies, these systems also have some unique features. Furthermore, it is a cost-effective software which is low on maintenance! Some of its features are:
We collected several images of vehicle’s license plates. We extract all kinds of images by extracting them from different sources or from machines (cameras) of different resolutions.
If our AI model learns to detect license plates within the vehicle’s images, we should feed the annotated dataset to the models to find the license plate part. So in the annotation process, annotators annotated license plates within the images by drawing bounding boxes around the license plates.
After completing the first phase of the annotation that is drawing bounding boxes to the vehicle’s license plates, our annotator team tagged letters, numbers, and symbols within the bounding boxes in the second phase.
If the annotation process is done correctly, it means all license plates are perfectly drawn and tagged with correct letters, numbers, and symbols. AI-Based models automatically learn good patterns to detect vehicle license plates. This annotation process will be helpful to the model to understand correct patterns and improve model performance.
The images extracted from the different sources required preprocessing before proceeding to the next step. Data preprocessing is the set of procedures used to apply on the raw extracted input images to transform them into clean versions, which could be fed to Artificial Intelligence models or neural network machine learning models.
In the image data preprocessing step, the image is converted into a grayscale image because the RGB (Red Green Blue) color image carries so much repetitive or irrelevant information that is not necessary for face mask detection. Then, we reshaped the images to keep the uniformity of the input images to the architecture. Then, the images are normalized, and after normalization, the value of a pixel stays in the range from 0 to 1. Normalization improved the learning algorithm to learn faster and captured the necessary features from the images.
License plate recognition is commonly a type of OCR (Optical Character Recognition) system. In short, OCR is a technology that allows you to convert various kinds of documents and images, such as scanned paper documents, PDF files, or images captured by a digital camera into editable and searchable data.
In our case, we are especially interested in license plates identification, and they contain letters and digits. Naturally, therefore, the OCR system has to read every sign found on a detected license plate.
The License Plate Recognition process consists of several stages that involve different algorithms.
Image acquisition is all about capturing and processing images and videos. Usually, CV (Computer Vision) algorithms work based on predefined rules related to finding patterns and identifying objects in an image. In our case, we’re looking for vehicles and their license plates.
Image localization means finding the License Plate within the whole image, disregarding unnecessary data, and focusing on the characters.
Image segmentation in the license plate detection separates the precise zone containing every character to identify them separately.
Character recognition from the different zones earlier identified.
Regional Syntactical Correction is to identify uncertainties on apparently identical characters. For example, I and 1, O and 0, B and 8, etc.
Building a License Plate Recognition model is a little tricky from understanding all license plates because license plates have unique characteristics based on each region (country or state).
Automatic License Plate Recognition has many applications since the license number is the primary, most broadly accepted, human-readable, necessary identifier of motor vehicles.
Below we showed some of the significant applications without the demand of completeness.
Police forces are already using AI-based License Plate Recognition systems for safety and law enforcement through fixed cameras on their vehicles or fixed control points to identify stolen, required, or uninsured vehicles.
License Plate Recognition allows creating touchless parking establishments where parkers don’t have to stop at barriers to enter, payor exit: a smoother, faster, and cleaner parking experience with minimum investment.
Gated Communities, companies, apartments, and governments use License Plate Recognition to automate and optimize their access control management in new ways. For example, they are automating vehicle access control by checking vehicle license plate numbers with an authorized database where authorized vehicle license plate numbers are stored.
Developing or smart Cities use AI-based License Plate Recognition systems to create better strategies based on a higher understanding or knowledge of the traffic.