  A growing demand for natural-scene text detection has been witnessed by the
computer vision community since text information plays a significant role in
scene understanding and image indexing. Deep neural networks are being used due
to their strong capabilities of pixel-wise classification or word localization,
similar to being used in common vision problems. In this paper, we present a
novel two-task network with integrating bottom and top cues. The first task
aims to predict a pixel-by-pixel labeling and based on which, word proposals
are generated with a canonical connected component analysis. The second task
aims to output a bundle of character candidates used later to verify the word
proposals. The two sub-networks share base convolutional features and moreover,
we present a new loss to strengthen the interaction between them. We evaluate
the proposed network on public benchmark datasets and show it can detect
arbitrary-orientation scene text with a finer output boundary. In ICDAR 2013
text localization task, we achieve the state-of-the-art performance with an
F-score of 0.919 and a much better recall of 0.915.
