  Textual information found in scene images provides high level semantic
information about the image and its context and it can be leveraged for better
scene understanding. In this paper we address the problem of scene text
retrieval: given a text query, the system must return all images containing the
queried text. The novelty of the proposed model consists in the usage of a
single shot CNN architecture that predicts at the same time bounding boxes and
a compact text representation of the words in them. In this way, the text based
image retrieval task can be casted as a simple nearest neighbor search of the
query text representation over the outputs of the CNN over the entire image
database. Our experiments demonstrate that the proposed architecture
outperforms previous state-of-the-art while it offers a significant increase in
processing speed.
