We present a neural network model, based on CNNs, RNNs and attention mechanisms, which achieves 84.04%% accuracy on the challenging French Street Name Signs (FSNS) dataset, significantly outperforming the previous state of the art (Smith’16), which achieved 72.46%%. Furthermore, our new method is much simpler and more general than the previous approach. To demonstrate the generality of our model, we also apply it to two datasets, derived from Google Street View, in which the goal is to extract business names from store fronts, and extract structured date/time information from parking signs. Finally, we study the speed/accuracy tradeoff that results from cutting pretrained inception CNNs at different depths and using them as feature extractors for the attention mechanism. The resulting model is not only accurate but efficient, allowing it to be used at scale on a variety of challenging real-world text extraction problems.