Recognition Number of The Vehicle Plate Using Otsu Method and K-Nearest Neighbour Classification

Maulidia Rahmah Hidayah, Isa Akhlis, Endang Sugiharti


The current topic that is interesting as a solution of the impact of public service improvement toward vehicle is License Plate Recognition (LPR), but it still needs to develop the research of LPR method. Some of the previous researchs showed that K-Nearest Neighbour (KNN) succeed in car license plate recognition. The Objectives of this research was to determine the implementation and accuracy of Otsu Method toward license plate recognition. The method of this research was Otsu method to extract the characteristics and image of the plate into binary image and KNN as recognition classification method of each character. The development of the license plate recognition program by using Otsu method and classification of KNN is following the steps of pattern recognition, such as input and sensing, pre-processing, extraction feature Otsu method binary, segmentation, KNN classification method and post-processing by calculating the level of accuracy. The study showed that this program can recognize by 82% from 100 test plate with 93,75% of number recognition accuracy and 91,92% of letter recognition accuracy.


Vehicle Plate, Image, KNN, LPR, Pattern Recognition

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