License plate recognition technology has become increasingly popular in recent years due to the wide range of advantages it offers for various applications. Traffic management, smart parking, intelligent transport systems in smart cities are some of the advantages it offers.

This installation is for the control of entry/exit in a paper factory. The procedure is simple: when the car approaches the checkpoint, the cameras recognize the vehicle from the license plate and the entrance gate opens. However, behind this seemingly simple process is an entire system that incorporates cutting-edge technology and a combination of innovations to simplify and automate the control. These are automatic number plate recognition (ANPR) systems that increasingly incorporate new technologies into their operation.

ANPR (automatic number plate recognition) is defined as the technology that identifies a vehicle by means of the number plate it uses. By using cameras, the technology is able to scan a plate and forward the required action to the system that manages that particular entry. This provides the opportunity for streamlined vehicle access control and reduces the need for manpower reinforcement given the autonomous operation offered.

The technology has seen significant growth in recent years, due to the range of benefits it offers in a number of different applications. Besides, these systems are among the most accurate and widely used computer vision solutions and the methods applied are constantly evolving to enhance the performance, accuracy, efficiency and scalability of the related software. The use of ANPR systems is becoming even more popular as technology advances rapidly with the advent of artificial intelligence and machine and deep learning.

There are a number of different techniques used in the context of adopting machine vision technologies.  The most important components of such a system include object detection using deep learning to identify and categories vehicles such as buses, cars, trucks, motorcycles, etc. Object detection algorithms then exploit neural networks which are trained on datasets. This is followed by the preparation of images which will be processed by the OCR algorithm. Since ANPR applications are usually implemented in challenging real-world environments, with varying lighting, weather conditions and inconsistent settings, image processing is used to contribute to improve sharpness and color correction.

In some cases, such as at night, cameras may produce images where details are not visible. However, the license plates are always captured in such a way that the ANPR software can easily interpret them and convert them into the required format. The most advanced cameras support embedded license plate recognition either through an on-board processing unit or through a cloud connection so that the captured images are forwarded to end-users with license plate data already attached to the data packet.

From all of the above, it is clear that a number of advantages emerge. Most of the benefits of ANPR come from automating manual tasks, highly efficient space management and enhancing the customer experience.  They are an important source of analytics, as the resulting data can be used for traffic analysis. This is particularly important for the operation of Intelligent Transport Systems, where data processing technologies are being adopted to improve the mobility of people and goods, manage demand, increase safety, reduce congestion and manage incidents efficiently.

The system offers flexibility as automated identification can be applied to a variety of applications. Also, with systems of this type helping to improve safety by providing a method for autonomous detection and monitoring of multiple vehicles.

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