Multi-Oriented Scene Text Detection via
Corner Localization and Region Segmentation
Abstract
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