Pattern recognition (psychology)

Mathematics

Pipeline (software)

Convolution (computer science)

Bounding overwatch

Operating system

Algorithm

Image (mathematics)

Geodesy

Minimum bounding box

Benchmark (surveying)

Artificial intelligence

Parameterized complexity

Geography

Bézier curve

Set (abstract data type)

Programming language

Geometry

Code (set theory)

Character (mathematics)

Spotting

Overhead (engineering)

Computer science

Computation

Artificial neural network

Segmentation

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 (\BeCan). Our contributions are three-fold: 1) For the first time, we adaptively fit oriented or curved 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 oriented or curved benchmark datasets, namely Total-Text and CTW1500, demonstrate that \BeCan achieves state-of-the-art accuracy, meanwhile significantly improving the speed. In particular, on Total-Text, our real-time version is over 10 times faster than recent state-of-the-art methods with a competitive recognition accuracy. Code is available at \url{https://git.io/AdelaiDet}.

ABCNet: Real-Time Scene Text Spotting With Adaptive Bezier-Curve Network