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.

A TextContextAware CNN Network for Multioriented and Multilanguage Scene Text Detection The existing deep learning based stateoftheart scene text detection methods treat scene texts a type of general objects, or segment text regions directly. The latter category achieves remarkable detection results on arbitraryorientation and large aspect ratios of scene texts based on instance segmentation algorithms. However, due to the lack of context information with consideration of scene text unique characteristics, directly applying instance segmentation to text detection task is prone to result in low accuracy, especially producing false positive detection results. To ease this problem, we propose a novel textcontextaware scene text detection CNN structure, which appropriately encodes channel and spatial attention information to construct contextaware and discriminative feature map for multioriented and multilanguage text detection tasks. With high representation ability of textcontextaware feature map, the proposed instance segmentation based method can not only robustly detect multioriented and multilanguage text from natural scene images, but also produce better text detection results by greatly reducing false positives. Experiments on ICDAR2015 and ICDAR2017MLT datasets show that the proposed method has achieved superior performances in precision, recall and Fmeasure than most of the existing studies.