MTRNet: A Generic Scene Text Eraser Text removal algorithms have been proposed for unilingual scripts with regular shapes and layouts. However, to the best of our knowledge, a generic text removal method which is able to remove all or userspecified text regions regardless of font, script, language or shape is not available. Developing such a generic text eraser for real scenes is a challenging task, since it inherits all the challenges of multilingual and curved text detection and inpainting. To fill this gap, we propose a maskbased text removal network (MTRNet). MTRNet is a conditional adversarial generative network (cGAN) with an auxiliary mask. The introduced auxiliary mask not only makes the cGAN a generic text eraser, but also enables stable training and early convergence on a challenging largescale synthetic dataset, initially proposed for text detection in real scenes. Whatu0027s more, MTRNet achieves stateoftheart results on several realworld datasets including ICDAR 2013, ICDAR 2017 MLT, and CTW1500, without being explicitly trained on this data, outperforming previous stateoftheart methods trained directly on these datasets.

TextEdge: Multioriented Scene Text Detection via Region Segmentation and Edge Classification The semanticsegmentationbased scene text detection algorithms always use the boundingbox regions or their shrinks to represent the text pixels. However, the nontext pixel information in these regions easily results in the poor performance of text detection, because these semantic segmentation methods need accurate pixellevel annotated training data to achieve approving performance and they are sensitive to noise and interference. In this work, we propose a fully convolutional network (FCN) based method termed TextEdge for multioriented scene text detection. Compared with previous methods simply using boundingbox regions as a segmentation mask, TextEdge introduces the textregion edge map as a new segmentation mask. Edge information is more representative for text areas and is proved to be effective in improving detection performance. TextEdge is optimized in an endtoend way with multitask outputs: text and nontext classification, textedge prediction and the text boundaries regression. Experiments on standard datasets demonstrate that the proposed method achieves stateoftheart performance in both accuracy and efficiency. Specifically, it achieves an Fscore of 0.88 on ICDAR 2013 dataset and 0.86 on ICDAR 2015 dataset.