A Novel Approach for License Plate Slant Correction, Character Segmentation and Chinese Character Recognition

In this paper, the methods of correcting skew vehicle license plate and segmenting characters in plate are discussed first. An approach making use of self-organizing map (SOM) is introduced to find the tilt angle of plate which simultaneously educes seven important points with coordinates being elements of weight matrix. After necessary processing to corrected plate, a character segmentation algorithm based on the shortest distance classification is presented, which takes advantage of exactly seven points gained from SOM as class centers. In the next place, a hybrid algorithm cascading two steps of template matching is utilized to recognize Chinese characters segmented from the license plates, which is based on the connected region feature and standard deviation feature extracted from sample corresponding to each template. Experimental results show that the proposed method can be implemented efficiently.