  Detecting curved text in the wild is very challenging. Recently, most
state-of-the-art methods are segmentation based and require pixel-level
annotations. We propose a novel scheme to train an accurate text detector using
only a small amount of pixel-level annotated data and a large amount of data
annotated with rectangles or even unlabeled data. A baseline model is first
obtained by training with the pixel-level annotated data and then used to
annotate unlabeled or weakly labeled data. A novel strategy which utilizes
ground-truth bounding boxes to generate pseudo mask annotations is proposed in
weakly-supervised learning. Experimental results on CTW1500 and Total-Text
demonstrate that our method can substantially reduce the requirement of
pixel-level annotated data. Our method can also generalize well across two
datasets. The performance of the proposed method is comparable with the
state-of-the-art methods with only 10% pixel-level annotated data and 90%
rectangle-level weakly annotated data.
