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. European, 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 contribution 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 (OCR)
method to obtain the final result. As an additional contribution, we also
present manual annotations for a challenging set of LP images from different regions and acquisition conditions. Our experimental results indicate
that the proposed method, without any parameter adaptation or fine
tuning for a specific scenario, performs similarly to state-of-the-art commercial systems in traditional scenarios, and outperforms both academic
and commercial approaches in challenging ones