Application Of Computer Vision And Pattern Recognition In License Plate Recognition

License Plate Recognition techniques have been widely used in modern transportation and play an active role in increasing productivity and reducing labor intensity.This study is based on the "License Plate Auto-Recognition System" project. It investigates related theories of computer vision and pattern recognition, designs a license plate recognition algorithm, and applies to real-world scenarios. The automatic license plate recognition is realized in this study.The basic license plate recognition can be classified into two categories: black and white (gray scale) image and color image. In contrast to black and white images, color images provide more information and better adaptation to surrounding environments. Multiple possible combinations of the color of license plate such as white words on blue background, black words on yellow background, black words on white background and so on necessities the analysis of various situations. However, for in-door license plate recognition, satisfactory results can be obtained by analyzing only black and white figure because of similar environments. This research focuses on black and white images, reduces the computational cost of classical algorithms for the transformation of RGB to HLS with a new fast conversion algorithm, and meets the real-time requirements of the system.Most of the classical image segmentation methods utilize binarization to differentiate object and its background. However, this method relies largely on the characteristics of both the object and background, and the quality of segmentation is easily impacted by the environment. This study proposes binarization after edge detecting, which can accurately separate license plate from its background and serve as the basis for locating and recognizing individual numbers and letters on license plates.At the recognition stage, we propose the combination of characteristic input and Multi-layer Perception Network (MLPN), which both reduces information input and improves self-study and fault tolerance of artificial neural network, resulting in a more precise recognition.