  This paper presents a scene text detection technique that exploits
bootstrapping and text border semantics for accurate localization of texts in
scenes. A novel bootstrapping technique is designed which samples multiple
'subsections' of a word or text line and accordingly relieves the constraint of
limited training data effectively. At the same time, the repeated sampling of
text 'subsections' improves the consistency of the predicted text feature maps
which is critical in predicting a single complete instead of multiple broken
boxes for long words or text lines. In addition, a semantics-aware text border
detection technique is designed which produces four types of text border
segments for each scene text. With semantics-aware text borders, scene texts
can be localized more accurately by regressing text pixels around the ends of
words or text lines instead of all text pixels which often leads to inaccurate
localization while dealing with long words or text lines. Extensive experiments
demonstrate the effectiveness of the proposed techniques, and superior
performance is obtained over several public datasets, e. g. 80.1 f-score for
the MSRA-TD500, 67.1 f-score for the ICDAR2017-RCTW, etc.
