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IRAQI LICENSE PLATE RECOGNITION BASED ON MACHINE LEARNING
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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.
College of Information Engineering - Al-Nahrain University
Title: IRAQI LICENSE PLATE RECOGNITION BASED ON MACHINE LEARNING
Description:
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.
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