Artificial intelligence systems are bridging the gap between real world and the digital world. AI and Machine Learning systems are performing the tasks that are considered important as well as more difficult. One such application of artificial intelligence is the automatic number plate recognition system. Number Plate recognition system detects the vehicle in the camera and captures the license plate image.
The License Plate Recognition System is based on the concept of optical character recognition to identify the number written on the number plate. Or, we can say that LPR takes an image of the vehicle as the input and outputs the number written on the number plate. This will help in getting the details of the vehicle and vehicle owner.
The License Plate Recognition System requires stationary cameras to be employed on streetlights, parking areas, signboards that can monitor the vehicles 24 hours a day. The LPR system is based on artificial intelligence applications in which machine have the capability to learn through experience and deliver better results. Some other core features supported are face detection, barcode reading, image recognition etc.
The LPR system is based on the concept of image recognition and Vision Framework. Let us first discuss them in brief.
Image recognition is the technology that can identify faces, buildings, places, objects, place in the images. Image recognition system is the sub-branch of computer vision that can identify or detect the objects in video or an image. Computer vision is a vast concept. It includes gathering, processing and analyzing the data from the real world. The image recognition system includes following three steps.
Now, let us have a look at the Vision Framework.
The number plate recognition system is based on the vision framework model. Vision framework is designed by Apple in a view to identify characters in images, face recognition, QR codes etc. The various features of vision framework are:
Now lets us have a look at how we can use vision framework for building the number plate recognition system application.
The ML model requires huge amount of data that is needed for the training of the ML model. First of all, we have to build the core ML model that can identify the number plates. The core model is trained using Keras, which is an open source neural network software based on Python.
The next step is to get the data which is in the form of images of character 28*28. The model is trained with the help of number plate images. The characters in the images were sliced and then converted into 28*28 resolutions. This process is automated with the help of the iOS application that can create thousands of character images with the help of vision framework. So, the basic approach followed in building LPR system is:
The authenticity of vehicles: The LPR system is helpful for the Police force to identify the authenticity of the vehicle by checking the number plate of the vehicle. This will help in mitigating the illegal activities by criminals. It also saves the drivers image in the database along with license plate number for future security tests. Also, this technology does not require to install any device on the vehicles, unlike any other technology.
Automation of Toll Barriers: LPR system can also be used in automation of the toll barriers. It will recognize the number plate and match it with the listed number plates in the database. The barrier will only open for the vehicles that are recognized by automatic toll collection barriers.
Some other important applications include:
Artificial intelligence application in number plate recognition system has numerous advantages. The solution is not only cost-effective but also quicker and needs no installation. Image recognition system and computer vision are assisting machine learning that can revolutionize the transport security system. Webtunix AI is Data Science consulting Company in India that is helping various organizations in building Artificial intelligence applications like LPR systems and applications in various sectors too.