  Multi-orientation scene text detection has recently gained significant
research attention. Previous methods directly predict words or text lines,
typically by using quadrilateral shapes. However, many of these methods neglect
the significance of consistent labeling, which is important for maintaining a
stable training process, especially when it comprises a large amount of data.
Here we solve this problem by proposing a new method, Orderless Box
Discretization (OBD), which first discretizes the quadrilateral box into
several key edges containing all potential horizontal and vertical positions.
To decode accurate vertex positions, a simple yet effective matching procedure
is proposed for reconstructing the quadrilateral bounding boxes. Our method
solves the ambiguity issue, which has a significant impact on the learning
process. Extensive ablation studies are conducted to validate the effectiveness
of our proposed method quantitatively. More importantly, based on OBD, we
provide a detailed analysis of the impact of a collection of refinements, which
may inspire others to build state-of-the-art text detectors. Combining both OBD
and these useful refinements, we achieve state-of-the-art performance on
various benchmarks, including ICDAR 2015 and MLT. Our method also won the first
place in the text detection task at the recent ICDAR2019 Robust Reading
Challenge for Reading Chinese Text on Signboards, further demonstrating its
superior performance. The code is available at https://git.io/TextDet.
