An efficient skew detection of license plate images based on wavelet transform and principal component analysis

Character segmentation and recognition are imperative steps in the vehicle license plate recognition (VLPR) system. The skewed license plate affects badly on the accurate character segmentation and recognition. To solve the problem, an efficient approach for skew correction of license plate is proposed based on wavelet transform and principal component analysis. First, a skew feature image of the original VLP image, which preserves the license plate's horizontal feature, is extracted by the two level wavelet transform. By applying a threshold, the skew feature image is then transformed in to a binary image which helps to identify the feature points which are considered as the edge of characters on the license plate with a lined regulation which reflects the slant angle of the plate useful for principal component analysis. Using principal component analysis the direction of the principal component, which gives the information about tilted angle of the plate, is achieved with the help of feature points, then the correction of the plate is accomplished. Main advantage of the proposed method is simplicity with less computationally complexity, which makes it useful for real time applications.