The advancement of text shape representations towards compactness has
enhanced text detection and spotting performance, but at a high annotation
cost. Current models use single-point annotations to reduce costs, yet they
lack sufficient localization information for downstream applications. To
overcome this limitation, we introduce Point2Polygon, which can efficiently
transform single-points into compact polygons. Our method uses a coarse-to-fine
process, starting with creating and selecting anchor points based on
recognition confidence, then vertically and horizontally refining the polygon
using recognition information to optimize its shape. We demonstrate the
accuracy of the generated polygons through extensive experiments: 1) By
creating polygons from ground truth points, we achieved an accuracy of 82.0% on
ICDAR 2015; 2) In training detectors with polygons generated by our method, we
attained 86% of the accuracy relative to training with ground truth (GT); 3)
Additionally, the proposed Point2Polygon can be seamlessly integrated to
empower single-point spotters to generate polygons. This integration led to an
impressive 82.5% accuracy for the generated polygons. It is worth mentioning
that our method relies solely on synthetic recognition information, eliminating
the need for any manual annotation beyond single points.

通过使用合成的识别信息，我们提出了 Point2Polygon 方法，将单点标注高效地转换为紧凑的多边形，并通过广泛实验证明了生成多边形的准确性。