Automatic Number Plate Recognition Using Artificial Neural Network

Automatic Number Plate Recognition (ANPR) became a very important tool in our daily life because of the unlimited increase of cars and transportation systems, which make it impossible to be fully managed and monitored by humans. Examples are so many, like traffic monitoring, tracking stolen cars, managing parking toll, red-light violation enforcement, border and customs checkpoints. Yet, it’s a very challenging problem, due to the diversity of plate formats, different scales, rotations and non-uniform illumination conditions during image acquisition. The objective of this paper is to provide a novel algorithm for license plate recognition in complex scenes, particularly for the all-day traffic surveillance environment. This is achieved using mathematical morphology and artificial neural network (ANN). A preprocessing step is applied to improve the performance of license plate localization and character segmentation in case of severe imaging conditions. The first and second stages utilize edge detection and mathematical morphology followed by connected component analysis. ANN is employed in the last stage to construct a classifier to categorize the input numbers of the license plate. The algorithm has been applied on 102 car images with different backgrounds, license plate angles, distances, lightening conditions, and colors. The average accuracy of the license plate localization is 97.06%, 95.10% for license plate segmentation, and 94.12% for character recognition. The experimental results show the outstanding detection performance of the proposed method comparing with traditional algorithms.