A Quadrilateral Scene Text Detector with Twostage Network Architecture Abstract   Many of the stateoftheart methods can only localize scene texts with rotated rectangle boundaries, which may result in incorrect rectification of the detected scene texts and erroneous elimination of proposals or detections during nonmaximum suppression (NMS). A few existing methods that can detect scene texts with quadrilateral boundaries, are just based on onestage architectures or sliding windows scanning and thus have suboptimal performance. To address these problems, we propose an endtoend twostage network architecture for scene text detection, which can accurately localize scene texts with quadrilateral boundaries. At the first stage, we propose a quadrilateral region proposal network (QRPN) for generating quadrilateral proposals, based on a newly proposed quadrilateral regression algorithm. At the second stage, we introduce a novel weighted RoI pooling module with learned weight masks to pool the features, and then classify the proposals and refine their shapes with the proposed quadrilateral regression algorithm again. Specially, during training, we adopt a dualbranch structure of detection heads, that is, jointly train the quadrilateral detection head and an additional rotated rectangle detection head. Furthermore, we develop an accelerated NMS algorithm with O(nlogn) complexity, for redundant quadrilateral text proposals and detections eliminating during the first and the second stage, respectively. Experiments on several challenging benchmarks demonstrate the superior performance of the proposed method, which achieves stateoftheart results on widely used benchmarks ICDAR 2017 MLT, RCTW, and ICDAR 2015 Incidental Scene Text benchmark.

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.