  Recently, Transformer-based methods, which predict polygon points or Bezier
curve control points for localizing texts, are popular in scene text detection.
However, these methods built upon detection transformer framework might achieve
sub-optimal training efficiency and performance due to coarse positional query
modeling.In addition, the point label form exploited in previous works implies
the reading order of humans, which impedes the detection robustness from our
observation. To address these challenges, this paper proposes a concise Dynamic
Point Text DEtection TRansformer network, termed DPText-DETR. In detail,
DPText-DETR directly leverages explicit point coordinates to generate position
queries and dynamically updates them in a progressive way. Moreover, to improve
the spatial inductive bias of non-local self-attention in Transformer, we
present an Enhanced Factorized Self-Attention module which provides point
queries within each instance with circular shape guidance. Furthermore, we
design a simple yet effective positional label form to tackle the side effect
of the previous form. To further evaluate the impact of different label forms
on the detection robustness in real-world scenario, we establish an
Inverse-Text test set containing 500 manually labeled images. Extensive
experiments prove the high training efficiency, robustness, and
state-of-the-art performance of our method on popular benchmarks. The code and
the Inverse-Text test set are available at
https://github.com/ymy-k/DPText-DETR.
