Generative Models for License Plate Recognition by Using a Limited Number of Training Samples

Increased mobility and internationalization open new challenges to develop effective traffic monitoring and control systems. This is true for automatic license plate recognition architectures that, nowadays, must handle plates from different countries with different character sets and syntax. While much emphasis has been put on the license plate localization and segmentation, little attention has been devoted to the huge amount of samples that are needed to train the character recognition algorithms. Nevertheless, these samples are difficult to get when dealing with an international-wide scenario that involves many different countries and the related legislations. This paper reports a new algorithm for license plate recognition, developed under a joint research funded by Autostrade per 1'Italia S.p.A., the main Italian highways company. The research aimed at achieving improved recognition rates when dealing with vehicles coming from different European and nearby states. Extensive experimental tests have been performed on a database of about 7.000 images comprising License Plates picked up by portals spread nationally. The overall rate of correct classification is 98.1

Study of object detection and reading(license plate detection and reading)

Object detection means finding the location of the object and recognizing what it is. The techniques used for the object detection are feature matching algorithm, pattern comparison and boundary detection. The feature matching algorithm is used to find the best matching object in the knowledge base and to implement the reconstruction of the object recognized. Our object detection is to detect the license plate detection of the car. To detect the license plate of a car first pre-process the image. The commonly license plate locating algorithms include line detection method, neural networks method, fuzzy logic vehicle license plate locating method. “Connected component analysis” is very easy technique than these techniques. In the pretreatment process we first crop the image. After this we convert the color image to gray level image. After converting into gray level that image is filtered using three different types of filters. They are Average, Median, Weiner filters. After deciding the good filter we will apply the segmentation process using edge detection. After finding the edges we will give the numbers to each connected component and store all the connected components in a matrix called labeling matrix. Extract the required connected component using the labeling matrix and store that in an image. Compare this template with our database using template matching technique. Template matching technique uses the correlation procedure. We will find the correlation coefficient between the two templates. Depending upon the correlation coefficient we will find that how much the two templates are similar to each other