Abstract
The license plate recognition (LPR) is an important system. LPR is helpful in many ranges such as private or public entrance, parking lots, traffic control and theft surveillance. This paper, offers (LPR) consist of four main stages (preprocessing, license plate detection, segmentation, character recognition) the first stage takes a photo by the camera then preprocessing in this image. License plate detection search for matching of license plate in the image to crop the correct plate. Segmentation performed by divide the numbers separately. The last stage is number recognition by using KNN (K- nearest neighbors) is one of the simple algorithms of machine learning used for matching numbers with training data to provide a correct prediction. The system was implemented using python3.5, open-cv library and shows accuracy performance result equal to 90% by using 50 images.
Highlights
In the last years, license plate recognition considers a core technology for security and traffic application that use in traffic control, parking lot and theft surveillance
Machine learning (ML) is a subset of Artificial Intelligence (AI) that allows software applications to be more careful in predicting outcomes without being candidly programmed
Each number/ character classify according to the classify that converted to English numbers /characters, which saved in a file. printing the last result of the license plate was on the image in English numbers/ characters, command window by using python GUI in Fig. 14 ( a & b & c)
Summary
License plate recognition considers a core technology for security and traffic application that use in traffic control, parking lot and theft surveillance. The old type before (2003) in Fig. 1, the second type for north Iraq, the last type the new Iraqi license plate in Fig. 3 has the character/ number in English and Arabic with different color such as the white color for private car, red for taxi, blue for government etc. The algorithm used in this paper is K- Nearest Neighbours (KNN) which is one of the machine learning algorithms It is supervised learning, a nonparametric technique and used for classifier and regression in several applications such as image processing, data mining, image recognition, and other application. A nonparametric technique and used for classifier and regression in several applications such as image processing, data mining, image recognition, and other application This algorithm is extremely easy and the output result of KNN depended on K number of training data closest to the required character / number and consider votes among K objects. Python shows all of this, which is why there are a lots of Python AI and machine learning projects today it’s used [5]
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