  In this paper, we first provide a new perspective to divide existing high
performance object detection methods into direct and indirect regressions.
Direct regression performs boundary regression by predicting the offsets from a
given point, while indirect regression predicts the offsets from some bounding
box proposals. Then we analyze the drawbacks of the indirect regression, which
the recent state-of-the-art detection structures like Faster-RCNN and SSD
follows, for multi-oriented scene text detection, and point out the potential
superiority of direct regression. To verify this point of view, we propose a
deep direct regression based method for multi-oriented scene text detection.
Our detection framework is simple and effective with a fully convolutional
network and one-step post processing. The fully convolutional network is
optimized in an end-to-end way and has bi-task outputs where one is pixel-wise
classification between text and non-text, and the other is direct regression to
determine the vertex coordinates of quadrilateral text boundaries. The proposed
method is particularly beneficial for localizing incidental scene texts. On the
ICDAR2015 Incidental Scene Text benchmark, our method achieves the F1-measure
of 81%, which is a new state-of-the-art and significantly outperforms previous
approaches. On other standard datasets with focused scene texts, our method
also reaches the state-of-the-art performance.
