Inverse-like Antagonistic Scene Text Spotting via Reading-Order
  Estimation and Dynamic Sampling
Shi-Xue Zhang Chun Yang Xiaobin Zhu Hongyang Zhou Hongfa Wang Xu-Cheng Yin
Scene text spotting is a challenging task, especially for inverse-like scene
text, which has complex layouts, e.g., mirrored, symmetrical, or retro-flexed.
In this paper, we propose a unified end-to-end trainable inverse-like
antagonistic text spotting framework dubbed IATS, which can effectively spot
inverse-like scene texts without sacrificing general ones. Specifically, we
propose an innovative reading-order estimation module (REM) that extracts
reading-order information from the initial text boundary generated by an
initial boundary module (IBM). To optimize and train REM, we propose a joint
reading-order estimation loss consisting of a classification loss, an
orthogonality loss, and a distribution loss. With the help of IBM, we can
divide the initial text boundary into two symmetric control points and
iteratively refine the new text boundary using a lightweight boundary
refinement module (BRM) for adapting to various shapes and scales. To alleviate
the incompatibility between text detection and recognition, we propose a
dynamic sampling module (DSM) with a thin-plate spline that can dynamically
sample appropriate features for recognition in the detected text region.
Without extra supervision, the DSM can proactively learn to sample appropriate
features for text recognition through the gradient returned by the recognition
module. Extensive experiments on both challenging scene text and inverse-like
scene text datasets demonstrate that our method achieves superior performance
both on irregular and inverse-like text spotting.