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.

A Text Localization Method Based on Weak Supervision Recently, numerous deep learning based scene text detection methods have achieved promising performances in different text detecting tasks. Most of these methods are trained in a supervised way, which requires a large amount of annotated data. In this paper, we explore a weakly supervised method to locate text regions in scene images. We propose a fully convolutional network (FCN) architecture to implement binary classification. The training data we used do not need any text location annotation, we only need to divide the training data into two categories according to whether it contains text or not. We can obtain the text localization map (TLM) directly from the last convolutional layer. By setting a fixed threshold, the TLM is converted to a mask map. Then the connected component analysis and the text proposals method based on Maximally Stable Extremal Regions (MSERs) are used to get the text region bounding boxes. We conduct comprehensive experiments on standard text datasets. The results show that our text localization method achieves comparable recall performance with other methods and has more stable property.