Detecting Text in News Images with Similarity Embedded Proposals Text extraction plays an important role in news images analysis tasks. Howerver, the conglutination of subtitles and station logos makes text detection challenging. In this paper, we develop an effective news text detection framework by introducing a novel similarity embedded proposal mechanism. The main idea is to predict similarity for each finescale coarse proposal to help construct text bounding boxes. Specifically, a CNN and bidirectional LSTM based network is used to produce vectors embedded in coarse proposals provided by Connectionist Text Proposal Network (CTPN). Notablely, similarity embedded proposal mechanism can be generalized to other subtext level text detection models. Comparing to the stateoftheart method (CTPN), our framework improves Fmeasure by 25.2% on our Private News Dataset and 8.9% on ICDAR 2013 benchmarks, respectively.

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.