Iranian License Plate Recognition using connected component and clustering techniques

License Plate Recognition system (LPR) plays a significant role in many application such as access control, traffic control, and the detection stolen vehicles. A LPR system can be divided into the detection and recognition stages. For license plate detection, a proposal method with to phase is used. At the first phase regions of around plate is clip out by help of vertical and horizontal projections. Next accurate location of plate is recognizing by connected component analysis and clustering techniques. Due to the positioning of vehicle towards the camera, the rectangular of license plate can be rotated and skewed in many ways. So skew detection and correction is requiring after plate detection. In this study an efficient method is proposed to skew detection and recognition. Zernike and wavelet moments features with rotation and scale invariant property are used to recognition of license plate characters. Proposed algorithms are robust to the different lighting condition, view angle, the position, size and color of the license plates when running in complicated environment. The overall performance of success for the license plate achieves 93.54% when the system is used to the license plate recognition in various conditions.