  Scene text detection and recognition has received increasing research
attention. Existing methods can be roughly categorized into two groups:
character-based and segmentation-based. These methods either are costly for
character annotation or need to maintain a complex pipeline, which is often not
suitable for real-time applications. Here we address the problem by proposing
the Adaptive Bezier-Curve Network (ABCNet). Our contributions are three-fold:
1) For the first time, we adaptively fit arbitrarily-shaped text by a
parameterized Bezier curve. 2) We design a novel BezierAlign layer for
extracting accurate convolution features of a text instance with arbitrary
shapes, significantly improving the precision compared with previous methods.
3) Compared with standard bounding box detection, our Bezier curve detection
introduces negligible computation overhead, resulting in superiority of our
method in both efficiency and accuracy. Experiments on arbitrarily-shaped
benchmark datasets, namely Total-Text and CTW1500, demonstrate that ABCNet
achieves state-of-the-art accuracy, meanwhile significantly improving the
speed. In particular, on Total-Text, our realtime version is over 10 times
faster than recent state-of-the-art methods with a competitive recognition
accuracy. Code is available at https://tinyurl.com/AdelaiDet
