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Although a polygon is a more accurate representation than an upright boundingbox for text detection, the annotations of polygons are extremely expensive andchallenging. Unlike existing works that employ fully-supervised training withpolygon annotations, this study proposes an unconstrained text detection systemtermed Polygon-free (PF), in which most existing polygon-based text detectors(e.g., PSENet [33],DB [16]) are trained with only upright bounding boxannotations. Our core idea is to transfer knowledge from synthetic data to realdata to enhance the supervision information of upright bounding boxes. This ismade possible with a simple segmentation network, namely Skeleton AttentionSegmentation Network (SASN), that includes three vital components (i.e.,channel attention, spatial attention and skeleton attention map) and one softcross-entropy loss. Experiments demonstrate that the proposed Polygonfreesystem can combine general detectors (e.g., EAST, PSENet, DB) to yieldsurprisingly high-quality pixel-level results with only upright bounding boxannotations on a variety of datasets (e.g., ICDAR2019-Art, TotalText,ICDAR2015). For example, without using polygon annotations, PSENet achieves an80.5% F-score on TotalText [3] (vs. 80.9% of fully supervised counterpart),31.1% better than training directly with upright bounding box annotations, andsaves 80%+ labeling costs. We hope that PF can provide a new perspective fortext detection to reduce the labeling costs. The code can be found athttps://github.com/weijiawu/Unconstrained-Text-Detection-with-Box-Supervisionand-Dynamic-Self-Training.

Polygon-free: Unconstrained Scene Text Detection with Box Annotations