License Plate Recognition using Optical Character Recognizer and Artificial neural network

License Number Plate Recognition (LNPR) 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 morphology and artificial neural network (ANN) with Optical Character Recognizer (OCR). 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 and Threshold based segmentation 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 average accuracy of the license plate localization is 99.76%. The experimental results show the outstanding detection performance of the proposed method comparing with traditional algorithms.