Abstract: Arbitrarily-shaped text detection faces two major challenges: 1) various scales and 2) irregular angles. Previous works regress the text boundary in Cartesian coordinates as ordinary object detection. However, such grid space interleaves the unique scale and angle attributes of text, which seriously affects detection performance. The implicit disregard of text scale also impairs multi-scale detection ability. To better learn the arbitrary text boundary and handle the text scale variation, we propose a novel Scale-Aware Polar Representation (SAPR) framework. The text boundary is represented in Polar coordinates, where scale and angle of text could be both clearly expressed for targeted learning. This simple but effective transformation brings significant performance improvement. The explicit learning on separated text scale also promotes the multi-scale detection ability. Based on the Polar representation, we design line IoU loss and symmetry sine loss to better optimize the scale and angle of text with a multi-path decoder architecture. Furthermore, an accurate center line calculation is proposed to guide text boundary restoration under various scales. Overall, the proposed SAPR framework is able to effectively detect arbitrarily-shaped texts and tackle the scale variation simultaneously. The state-of-the-art results on multiple benchmarks solidly demonstrate the effectiveness and superiority of SAPR.