A New Parallel DetectionRecognition Approach for EndtoEnd Scene Text Extraction In this work, we present a new conceptually simple and flexible network for accurate scene text extraction, which handle text detection and text recognition concurrently. Apart from solving feature sharing by designing a unified network, we implement endtoend training of the whole system by a set of new optimization strategies. More importantly, this method highlights its novel parallel detectionrecognition structure, which constructs a loose connection between both detection and recognition. This loose connection is embodied in the definition of overall loss and derivative back propagation for the model parameter updating, which automatically balances the contribution of the two branches to the system performance. It is different from the existing endtoend methods where two subtasks are connected serially and thus yielding heavy dependence of the predecessor text detection task on the followup text recognition task and sensitivity of recognition to detection noise. In addition, a simple MaskRectifier mechanism is applied to easily adapt our system to incidental text recognition with arbitrary orientation and shape. Experiment results on Incidental Scene Text ICDAR2015 dataset surpass the current stateoftheart FOTS method, as suggests the effectiveness of the proposed approach.

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.