Generally speaking, human beings can recognize objects without too much effort or consideration. However, recognizing objects in images can be a very difficult task in the field of computer vision. There are many use cases in which this difficulty becomes evident. One specific use case involves license plate detection—it’s particularly a big challenge because of the differences inherent in the plates (i.e. the objects) themselves: different sizes and styles, the conditions and lighting under which the images of the plates are captured, etc.
Nowadays, there are many commercial systems that involve license plate recognition, and it can be used in many use cases such as:
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Finding stolen cars: This kind of system can be deployed on the roadside, and makes a real-time comparison between passing cars and the list of stolen cars. When a match is found, an alert is issued to inform the police officer of the car detected and the reasons for stopping the car.
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Parking: The detected license plate number is used in car parks in order to calculate parking fees by comparing entry and exit times.
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Toll: The car number is used to calculate travel costs on a toll road, or used to re-check tickets.
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Access control: The automatic opening of a door for authorized members in a safety zone. This kind of system is set up to help security officers. Events are stored on a database and can be used to search event history when needed.
Run the following File
main.ipynb
Opencv,Numpy,Os,Math,Random,CSV
PIL,Tkinter
The Project consists of following steps-
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Preprocessing
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Localization of number plate
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Character Recognition
Flowchart of how Project works:
Detailed Explanation of Project is given in Video