  The research focus of scene text detection and recognition has shifted to
arbitrary shape text in recent years, where the text shape representation is a
fundamental problem. An ideal representation should be compact, complete,
efficient, and reusable for subsequent recognition in our opinion. However,
previous representations have flaws in one or more aspects. Thin-Plate-Spline
(TPS) transformation has achieved great success in scene text recognition.
Inspired by this, we reversely think of its usage and sophisticatedly take TPS
as an exquisite representation for arbitrary shape text representation. The TPS
representation is compact, complete, and efficient. With the predicted TPS
parameters, the detected text region can be directly rectified to a
near-horizontal one to assist the subsequent recognition. To further exploit
the potential of the TPS representation, the Border Alignment Loss is proposed.
Based on these designs, we implement the text detector TPSNet, which can be
extended to a text spotter conveniently. Extensive evaluation and ablation of
several public benchmarks demonstrate the effectiveness and superiority of the
proposed method for text representation and spotting. Particularly, TPSNet
achieves the detection F-Measure improvement of 4.4\% (78.4\% vs. 74.0\%) on
Art dataset and the end-to-end spotting F-Measure improvement of 5.0\% (78.5\%
vs. 73.5\%) on Total-Text, which are large margins with no bells and whistles.
