A - Automatic
N - Number
P - Plate
R - Recognition

Automatic Number Plate Recognition (ANPR)


ANPR (Automatic Number Plate Recognition) is a special form of optical character recognition. License plate recognition is a type of technology that enables the computer system to read automatically the registration number plate of vehicles. ANPR technology is used to read automatically the registration number of the vehicles. It can use the CCTV cameras to read vehicle registration number.

Why ANPR System is used?

ANPR system can be used for a variety of applications. Let us have a look at the uses of the ANPR system.

Authentication of Vehicles

The Automatic number plate recognition system can be used to identify the authenticity of the vehicle by checking the number plate of the vehicle. This can help in eliminating the illegal activities by the criminals. ANPR technology can read and license plate and store it in the database for future reference.

Automation in Toll barriers

ANPR systems can be used in malls and toll barriers to add the number plate to their database. The ANPR system will recognize the number plate and match it with the listed number plates in the database. The barrier will open for the vehicles whose number plate match with the entry in the database.

ANPR systems can be used in other applications like:

  • Recovery of the stolen cars
  • Used in malls for parking purpose
  • Identify the cars that do not belong to a particular parking area
  • Check the speed of the car by calculating the time it takes to cross from camera A to B


How ANPR system Works?

ANPR system is based on the concept of image recognition and vision framework. Image recognition is the applilcation of computer vision technology which is able to identify faces, buildings, places and objects in the images. Image recognition technology can detect the objects in video or images. Computer vision is a vast concept and it includes gathering the data from the real world.

1. Collect the Images

The first step is to collect and recognize the images. The data can be collected from various sources like internet or paid services. The collected images need to be organized because these images includes lots of non important information.

2. Build a predictive model

In the next step, the classification algorithm takes the vector image as input and gives output in the form of a class label. The classification algiorithm is first trained by a huge amount of dataset. This also helps in building the predictive model. The predictive model also requires neural network for its operation. There are numerous algorithms for image classification. For e.g. support vector machines, K-nearest neighbour (KNN), logistic regression etc.

3. Recognize the images

This is the last step in image recognition system. In this step, the training data, image data, and test data are organized. In this step, duplicate images are to be removed. The data is then fed to the model to recognize the images. The classification system identifies the images by matching it to the database and we get the desired output.

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