A Relation Network Based Approach to Curved Text Detection In this paper, a new relation network based approach to curved text detection is proposed by formulating it as a visual relationship detection problem. The key idea is to decompose curved text detection into two subproblems, namely detection of text primitives and prediction of link relationship for each nearby text primitive pair. Specifically, an anchorfree region proposal network based text detector is first used to detect text primitives of different scales from different feature maps of a feature pyramid network, from which a manageable number of text primitive pairs are selected. Then, a relation network is used to predict whether each text primitive pair belongs to a same text instance. Finally, isolated text primitives are grouped into curved text instances based on link relationships of text primitive pairs. Because pairwise link prediction has used features extracted from the bounding boxes of each text primitive and their union, the relation network can effectively leverage wider context information to improve link prediction accuracy. Furthermore, since the link relationships of relatively distant text primitives can be predicted robustly, our relation network based text detector is capable of detecting text instances with large intercharacter spaces. Consequently, our proposed approach achieves superior performance on not only two public curved text detection datasets, namely TotalText and SCUTCTW1500, but also a multioriented text detection dataset, namely MSRATD500.

Curved Text Detection in Natural Scene Images with Semi and WeaklySupervised Learning Detecting curved text in the wild is very challenging. Recently, most stateoftheart methods are segmentation based and require pixellevel annotations. We propose a novel scheme to train an accurate text detector using only a small amount of pixellevel annotated data and a large amount of data annotated with rectangles or even unlabeled data. A light model is first obtained by training with the pixellevel annotated data and then used to annotate unlabeled or weakly labeled data. A novel strategy which utilizes groundtruth bounding boxes to generate pseudo mask annotations is proposed in weaklysupervised learning. Experimental results on CTW1500 and TotalText demonstrate that our method can substantially reduce the requirement of pixellevel annotated data. Our method can also generalize well across the two datasets. The performance of the proposed method is comparable with the stateoftheart methods with only 10% pixellevel annotated data and 90% rectanglelevel weakly annotated data.