TPSNet: Thin-Plate-Spline Representation for Arbitrary Shape Scene Text Detection

The research focus of scene text detection has shifted to arbitrary shape text in recent years, in which text representation is a fundamental problem. An ideal representation should be compact, complete, integral, and reusable for subsequent recognition in our opinion. However, previous representations suffer from one or several aspects. Thin-Plate-Spline (TPS) transformation has achieved great success in scene text recognition. Inspired from this, we reversely think its usage and sophisticatedly take TPS as an exquisite representation for arbitrary shape text detection. The TPS representation is compact, complete and integral, and with the predicted TPS parameters, the detected text region can be rectified to near-horizontal one which is beneficial for subsequent recognition. To solve the supervision problem of TPS training without key point annotations, two novel losses including the boundary set loss and the shape alignment loss are proposed. Extensive evaluation and ablation on several public benchmarks demonstrate the effectiveness and superiority of the proposed method.