The Research Of License Plate Recognition Algorithm Based On Machine Vision

License plate recognition (PLR) system is an important part of intelligent transportation system, which is mostly applied to electronic payment system, such as non-stop toll collection in highway, non-attended parking fee payment, and multi-use payment. LPR system has been developed for many years. The two factors of the complicated conditions and various illuminations that affect the quality of scene images are the techniques puzzles to be solved. The three models of license plate located, characters segmented, and characters recognition are researched, then the relative algorithm is proposed in order to solve the relative techniques. Research achievements and innovations are as follow:The colorful image which is changed into the gray one, image enhancement, median-filter and so on, are analyzed and summarized in the plate image recognition application characters. Searching for the features of the object is designed based on the features of gray arrangement. The plate is located rough by the color feature and the plate information according to the geometry feature of the plates. Then the plate is located accurately by the way of searching for the object feature points according to the boundary of the white and black pixels. Then the binarization of the image is obtained by the histogram feature analysis method. In addition, the incline angle is found by the rotation projection method, then the incline of the image is adjusted; Moreover, the rim of the image is wiped off in order to satisfy the requirement of the plate location.The flyback segmentation and vertical projection are used to deal with the revised image. The characters are isolated according to the geometry feature of the plate to find the position of the single character. Next, the single character measure feature is used to isolate the conglutination of the characters. The adjusted actual plate width is applied aiming at the incline characters. The flyback segmentation is used to isolate the Chinese characters made up of the two elements or three elements. Then, the method of templates matching is compared with the method told above. The method in this article acquires a good segmentation according to the experiments.Analysing the pattern recognition of multipliable templates modeling based on the Taylor formula(MTMT) and the statistical pattern recognition based on traditional templates matching(TTM), the improvement is projected according to the factual condition. An algorithm combined the pattern recognition of multipliable templates modeling based on the Taylor formula(MTMT) and the statistical pattern recognition based on traditional templates matching(TTM) is used to recognize the characters, aiming at the nonuniform illumination. Then, the pattern recognition of multipliable templates modeling based on the Taylor formula(MTMT) uses the robust regression to minimize the feature errors, and the recognition result is obtained by analyzing the reconstructed weights errors. Moreover, the characters that are easy to recognize wrong is recognized secondly by TTM. In addition, the characters recognized wrong and the typical characters are able to add to the swatch in order to raise the recognition rate.