Automatic Number Plate Recognition System

Pretty Joy, Linda Sebastian, Anitha Abraham

Abstract: The traffic on the roads is increasing day by day. There is dire need of developing an automation system that can effectively manage and control the traffic on roads. The traffic data of multiple vehicle types on roads is important for taking various decisions related to traffic. A video based traffic data collection system for multiple vehicle types is helpful for monitoring vehicles under homogenous and heterogeneous traffic conditions. License plate recognition (LPR) algorithms in images or videos are generally composed of processing steps such as extraction of a license plate region, segmentation of the plate characters and recognition of each character. This task is quite challenging due to the diversity of plate formats and the nonuniform outdoor illumination conditions during image acquisition. Therefore, most approaches work only under restricted conditions such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Numerous techniques have been developed for LPR in still images or video sequences, and the purpose of this paper is to categorize and assess them. Issues such as processing time, computational power, and recognition rate are also addressed.

Keywords: License plate detection, Neural network, Recognition, Segmentation, Template matching