  One of the main challenges for arbitrary-shaped text detection is to design a
good text instance representation that allows networks to learn diverse text
geometry variances. Most of existing methods model text instances in image
spatial domain via masks or contour point sequences in the Cartesian or the
polar coordinate system. However, the mask representation might lead to
expensive post-processing, while the point sequence one may have limited
capability to model texts with highly-curved shapes. To tackle these problems,
we model text instances in the Fourier domain and propose one novel Fourier
Contour Embedding (FCE) method to represent arbitrary shaped text contours as
compact signatures. We further construct FCENet with a backbone, feature
pyramid networks (FPN) and a simple post-processing with the Inverse Fourier
Transformation (IFT) and Non-Maximum Suppression (NMS). Different from previous
methods, FCENet first predicts compact Fourier signatures of text instances,
and then reconstructs text contours via IFT and NMS during test. Extensive
experiments demonstrate that FCE is accurate and robust to fit contours of
scene texts even with highly-curved shapes, and also validate the effectiveness
and the good generalization of FCENet for arbitrary-shaped text detection.
Furthermore, experimental results show that our FCENet is superior to the
state-of-the-art (SOTA) methods on CTW1500 and Total-Text, especially on
challenging highly-curved text subset.
