  This paper proposes a region based convolutional neural network for
segmentation-free word spotting. Our net- work takes as input an image and a
set of word candidate bound- ing boxes and embeds all bounding boxes into an
embedding space, where word spotting can be casted as a simple nearest
neighbour search between the query representation and each of the candidate
bounding boxes. We make use of PHOC embedding as it has previously achieved
significant success in segmentation- based word spotting. Word candidates are
generated using a simple procedure based on grouping connected components using
some spatial constraints. Experiments show that R-PHOC which operates on images
directly can improve the current state-of- the-art in the standard GW dataset
and performs as good as PHOCNET in some cases designed for segmentation based
word spotting.
