Abstract
Automatic license plate recognition plays an essential role in many applications and a number of methods have been proposed. These applications range from complex security systems to common areas and from parking admission to urban traffic control. License plate recognition has hard properties due to varied effects as fog, rain, shadows, irregular illumination conditions, variable distances, cars' velocity, scene's angle on frame, plate rotation and conservation, number of vehicles in the scene and other. These effects make plate recognition much more complex and difficult than the traditional pattern recognition systems. The proposed system is composed of the following three stages: 1) detection and extraction of a license plate area by video camera 2) segmentation of the plate characters and digits; and 3) character and digit recognition. The plate image area extracted using the proposed system is segmented into disjoint characters for the character recognition phase, where the neural network classifier is used to recognize the characters based on edge moment invariants and principal component analysis features of wavelet coefficient matrix. The main goal of this research is to develop a new plate recognition system with intelligent issues surpass the systems introduced in literature and to reduce many of the restrictions in the working environment. here the part of summary.