  In this paper, we propose a novel approach for text detec- tion in natural
images. Both local and global cues are taken into account for localizing text
lines in a coarse-to-fine pro- cedure. First, a Fully Convolutional Network
(FCN) model is trained to predict the salient map of text regions in a holistic
manner. Then, text line hypotheses are estimated by combining the salient map
and character components. Fi- nally, another FCN classifier is used to predict
the centroid of each character, in order to remove the false hypotheses. The
framework is general for handling text in multiple ori- entations, languages
and fonts. The proposed method con- sistently achieves the state-of-the-art
performance on three text detection benchmarks: MSRA-TD500, ICDAR2015 and
ICDAR2013.
