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%