Abstract
Abstract Our purpose of this research is to utilize the measures of orthogonal projection and tree decisions on vehicle license plate recognition (LPR). Our research uses orthogonal projection as the core of character recognition. In order to raise the recognition rate, tree decisions based on features of character contours and projection profiles are employed. This thesis consists of three main parts. The first part is to locate the license plate in an image. The second part is to detect and segment each character of the license plate. The third part is on character recognition of license plates. Character recognition is more concerned in our research. Horizontal and vertical projections on orthogonal axes are adopted as comparisons between input characters and standard ones. An argument called cumulative difference values (CDV) is introduced to give a solution from standard database. Tree decisions are added to assist in distinguishing characters. The recognition rate from 59 images is 86.44%, and the recognition rate of 312 characters is 99.68%.Keywords: license plate recognition, LPR, optical character recognition (OCR), character segmentation, orthogonal projection, cumulative difference values (CDV), tree decisions.