  Traditional text detection methods mostly focus on quadrangle text. In this
study we propose a novel method named sliding line point regression (SLPR) in
order to detect arbitrary-shape text in natural scene. SLPR regresses multiple
points on the edge of text line and then utilizes these points to sketch the
outlines of the text. The proposed SLPR can be adapted to many object detection
architectures such as Faster R-CNN and R-FCN. Specifically, we first generate
the smallest rectangular box including the text with region proposal network
(RPN), then isometrically regress the points on the edge of text by using the
vertically and horizontally sliding lines. To make full use of information and
reduce redundancy, we calculate x-coordinate or y-coordinate of target point by
the rectangular box position, and just regress the remaining y-coordinate or
x-coordinate. Accordingly we can not only reduce the parameters of system, but
also restrain the points which will generate more regular polygon. Our approach
achieved competitive results on traditional ICDAR2015 Incidental Scene Text
benchmark and curve text detection dataset CTW1500.
