Automated license plate recognition (ALPR) technology is a powerful technology enabling more efficient and effective law enforcement, security, payment collection, and research. A common license plate standard was adopted by the member states of the Mercosur trading bloc (Argentina, Brazil, Paraguay and Uruguay) and consequently requires an upgrade to the ALPR software used by law enforcement and industry. Due to the scarcity of real license plate images, training state-of-the-art supervised detectors is unfeasible unless data augmentation techniques and synthetic training data are used. This paper presents an accurate and efficient automated Mercosur license plate detector using a Convolutional Neural Network (CNN) trained exclusively with synthetic imagery. In order to obtain the synthetic training data, Mercosur license plates were faithfully reproduced. Digital image processing techniques were employed to reduce the domain gap and a CNN with basic image manipulation was used to embed the artificial licensed plates in to realistic contexts. The trained model was then validated on real images captured from a parking lot and a publicly available traffic monitoring video stream. The results of experiments suggest detection accuracy of about 95% and an average running time of 40 milliseconds.