  We present a novel single-shot text detector that directly outputs word-level
bounding boxes in a natural image. We propose an attention mechanism which
roughly identifies text regions via an automatically learned attentional map.
This substantially suppresses background interference in the convolutional
features, which is the key to producing accurate inference of words,
particularly at extremely small sizes. This results in a single model that
essentially works in a coarse-to-fine manner. It departs from recent FCN- based
text detectors which cascade multiple FCN models to achieve an accurate
prediction. Furthermore, we develop a hierarchical inception module which
efficiently aggregates multi-scale inception features. This enhances local
details, and also encodes strong context information, allow- ing the detector
to work reliably on multi-scale and multi- orientation text with single-scale
images. Our text detector achieves an F-measure of 77% on the ICDAR 2015 bench-
mark, advancing the state-of-the-art results in [18, 28]. Demo is available at:
http://sstd.whuang.org/.
