  Although a polygon is a more accurate representation than an upright bounding
box for text detection, the annotations of polygons are extremely expensive and
challenging. Unlike existing works that employ fully-supervised training with
polygon annotations, this study proposes an unconstrained text detection system
termed Polygon-free (PF), in which most existing polygon-based text detectors
(e.g., PSENet [33],DB [16]) are trained with only upright bounding box
annotations. Our core idea is to transfer knowledge from synthetic data to real
data to enhance the supervision information of upright bounding boxes. This is
made possible with a simple segmentation network, namely Skeleton Attention
Segmentation Network (SASN), that includes three vital components (i.e.,
channel attention, spatial attention and skeleton attention map) and one soft
cross-entropy loss. Experiments demonstrate that the proposed Polygonfree
system can combine general detectors (e.g., EAST, PSENet, DB) to yield
surprisingly high-quality pixel-level results with only upright bounding box
annotations on a variety of datasets (e.g., ICDAR2019-Art, TotalText,
ICDAR2015). For example, without using polygon annotations, PSENet achieves an
80.5% F-score on TotalText [3] (vs. 80.9% of fully supervised counterpart),
31.1% better than training directly with upright bounding box annotations, and
saves 80%+ labeling costs. We hope that PF can provide a new perspective for
text detection to reduce the labeling costs. The code can be found at
https://github.com/weijiawu/Unconstrained-Text-Detection-with-Box-Supervisionand-Dynamic-Self-Training.
