  Scene text detection remains a grand challenge due to the variation in text
curvatures, orientations, and aspect ratios. One of the hardest problems in
this task is how to represent text instances of arbitrary shapes. Although many
methods have been proposed to model irregular texts in a flexible manner, most
of them lose simplicity and robustness. Their complicated post-processings and
the regression under Dirac delta distribution undermine the detection
performance and the generalization ability. In this paper, we propose an
efficient text instance representation named CentripetalText (CT), which
decomposes text instances into the combination of text kernels and centripetal
shifts. Specifically, we utilize the centripetal shifts to implement pixel
aggregation, guiding the external text pixels to the internal text kernels. The
relaxation operation is integrated into the dense regression for centripetal
shifts, allowing the correct prediction in a range instead of a specific value.
The convenient reconstruction of text contours and the tolerance of prediction
errors in our method guarantee the high detection accuracy and the fast
inference speed, respectively. Besides, we shrink our text detector into a
proposal generation module, namely CentripetalText Proposal Network, replacing
Segmentation Proposal Network in Mask TextSpotter v3 and producing more
accurate proposals. To validate the effectiveness of our method, we conduct
experiments on several commonly used scene text benchmarks, including both
curved and multi-oriented text datasets. For the task of scene text detection,
our approach achieves superior or competitive performance compared to other
existing methods, e.g., F-measure of 86.3% at 40.0 FPS on Total-Text, F-measure
of 86.1% at 34.8 FPS on MSRA-TD500, etc. For the task of end-to-end scene text
recognition, our method outperforms Mask TextSpotter v3 by 1.1% on Total-Text.
