License plate detection is a challenging task when dealing with open environments and images captured from a certain distance by lowcost cameras. In this paper, we propose an approach for detecting license plates based on a convolutional neural network which models a function that produces a score for each image sub-region, allowing us to estimate the locations of the detected license plates by combining the results obtained from sparse overlapping regions. Experiments were performed on a challenging benchmark, containing 4,070 license plates in 1,829 images, captured under several weather conditions. The proposed approach achieved a precision of 0.87 and recall of 0.83, outperforming a state-of-the-art detector—a promising result, given that the experiments were performed on single images, without any kind of preprocessing or temporal integration.