  Previous deep learning based state-of-the-art scene text detection methods
can be roughly classified into two categories. The first category treats scene
text as a type of general objects and follows general object detection paradigm
to localize scene text by regressing the text box locations, but troubled by
the arbitrary-orientation and large aspect ratios of scene text. The second one
segments text regions directly, but mostly needs complex post processing. In
this paper, we present a method that combines the ideas of the two types of
methods while avoiding their shortcomings. We propose to detect scene text by
localizing corner points of text bounding boxes and segmenting text regions in
relative positions. In inference stage, candidate boxes are generated by
sampling and grouping corner points, which are further scored by segmentation
maps and suppressed by NMS. Compared with previous methods, our method can
handle long oriented text naturally and doesn't need complex post processing.
The experiments on ICDAR2013, ICDAR2015, MSRA-TD500, MLT and COCO-Text
demonstrate that the proposed algorithm achieves better or comparable results
in both accuracy and efficiency. Based on VGG16, it achieves an F-measure of
84.3% on ICDAR2015 and 81.5% on MSRA-TD500.
