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. Automatic Number Plate recognition system detects the vehicle in the camera and captures the license plate image.
What is Automatic Number Plate Recognition System?
The Automatic Number 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 Automatic Number 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.
Building the application for License plate recognition
The LPR system is based on the concept of image recognition and Vision Framework. Let us first discuss them in brief.
Image recognition system
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.
- Organize and Identify Images: The first step in image recognition system is to organize and identify the images. Computer vision imitates the human vision to identify the objects in the images. The computer analyzes the image in the form of raster or vector image. A raster is a number of pixels with discrete numerical values. The first step in identifying the images is to classify the images by extracting the important information and leaving the non-important information.
- Build a predictive model: In this step, the classification algorithm takes vector images as the input and gives output in the form of a class label. The classification algorithm is first trained by the huge amount of datasets. This helps in building the predictive model. The predictive model also requires neural networks for their operation. There are numerous algorithms for image classification. For e.g. K-nearest neighbour (KNN), support vector machines, logistic regression etc.
- Recognize 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.
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:
- Recognizes characters in the images.
- Finding and identifying the barcodes.
- Process the images with core ML Model.
- Detecting the face rectangle and facial landmarks
Now lets us have a look at how we can use vision framework for building the number plate recognition system application.
- Create the Core ML model
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 implementation
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:
- Train model in any framework of your choice
- Covert the model into ML model using Python
- User the model in the application.
Applications of Licence Plate recognition System
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:
- Recovery of stolen cars
- Identifying criminals that are having an open arrest warrant
- Check the speed of the car by calculating the time it takes to cross from camera A to B.
- Identifying the cars that do not belong to a particular parking area.
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.