A Sliding and Classifying Approach Towards Real Time Persian License Plate Recognition

Automatic license plate recognition, alpr, is an important part of today’s traffic monitoring and toll-gate systems. it usually consists of two major parts: plate localization and character recognition. in this paper, we propose a real-time algorithm for detection and recognition of persian license plates in four styles. to be real time, we employ simple and effective techniques and implement our algorithm in pure c^++. unlike conventional methods for finding the location of the plate based on structural features, we use a sliding and classifying approach combined with some statistical information. in recognition phase, two fast and accurate features are trained by a neural network. the proposed system is evaluated on 100 images of iranian vehicles, taken from different highway/toll-gate cameras. in localization phase, system detects 100% of the plates properly and in recognition phase, 97.8% of characters are correctly recognized. the overall processing time for single plate is 0.06 s at resolution of 407x309 and 0.24 s at resolution of 1150×650. these specifications made our algorithm industryready and currently. it is used by several corporations working on parking management and law enforcement systems