PBFormer: Capturing Complex Scene Text Shape with Polynomial Band
  Transformer
Ruijin Liu Ning Lu Dapeng Chen Cheng Li Zejian Yuan Wei Peng
We present PBFormer, an efficient yet powerful scene text detector that
unifies the transformer with a novel text shape representation Polynomial Band
(PB). The representation has four polynomial curves to fit a text's top,
bottom, left, and right sides, which can capture a text with a complex shape by
varying polynomial coefficients. PB has appealing features compared with
conventional representations: 1) It can model different curvatures with a fixed
number of parameters, while polygon-points-based methods need to utilize a
different number of points. 2) It can distinguish adjacent or overlapping texts
as they have apparent different curve coefficients, while segmentation-based or
points-based methods suffer from adhesive spatial positions. PBFormer combines
the PB with the transformer, which can directly generate smooth text contours
sampled from predicted curves without interpolation. A parameter-free
cross-scale pixel attention (CPA) module is employed to highlight the feature
map of a suitable scale while suppressing the other feature maps. The simple
operation can help detect small-scale texts and is compatible with the
one-stage DETR framework, where no postprocessing exists for NMS. Furthermore,
PBFormer is trained with a shape-contained loss, which not only enforces the
piecewise alignment between the ground truth and the predicted curves but also
makes curves' positions and shapes consistent with each other. Without bells
and whistles about text pre-training, our method is superior to the previous
state-of-the-art text detectors on the arbitrary-shaped text datasets.