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 this https URL

本研究提出在文字检测和识别中应用自适应贝塞尔曲线网络 (ABCNet) 以适应任意形状文字，通过设计新颖的 BezierAlign 层提取准确的卷积特征。ABCNet 相较于现存方法具有更高的速度和准确性，适用于实时应用。