License Plate Detection and Recognition in Unconstrained Scenarios Sérgio Montazzolli Silva[0000−0003−2444−3175] and Cláudio Rosito Jung[0000−0002−4711−5783] Institute of Informatics - Federal University of Rio Grande do Sul Porto Alegre, Brazil {smsilva,crjung}@inf.ufrgs.br Abstract. Despite the large number of both commercial and academic methods for Automatic License Plate Recognition (ALPR), most existing approaches are focused on a specific license plate (LP) region (e.g. Eu- ropean, US, Brazilian, Taiwanese, etc.), and frequently explore datasets containing approximately frontal images. This work proposes a complete ALPR system focusing on unconstrained capture scenarios, where the LP might be considerably distorted due to oblique views. Our main contribu- tion is the introduction of a novel Convolutional Neural Network (CNN) capable of detecting and rectifying multiple distorted license plates in a single image, which are fed to an Optical Character Recognition
