  We present a neural network model - based on CNNs, RNNs and a novel attention
mechanism - which achieves 84.2% 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 show that it also performs well on an even more challenging
dataset derived from Google Street View, in which the goal is to extract
business names from store fronts. Finally, we study the speed/accuracy tradeoff
that results from using CNN feature extractors of different depths.
Surprisingly, we find that deeper is not always better (in terms of accuracy,
as well as speed). Our resulting model is simple, accurate and fast, allowing
it to be used at scale on a variety of challenging real-world text extraction
problems.
