An EndtoEnd Video Text Detector with Online Tracking Video text detection is considered as one of the most difficult tasks in document analysis due to the following two challenges: 1) the difficulties caused by video scenes, i.e., motion blur, illumination changes, and occlusion; 2) the properties of text including variants of fonts, languages, orientations, and shapes. Most existing methods attempt to enhance the performance of video text detection by cooperating with video text tracking, but treat these two tasks separately. In this work, we propose an endtoend video text detection model with online tracking to address these two challenges. Specifically, in the detection branch, we adopt ConvLSTM to capture spatial structure information and motion memory. In the tracking branch, we convert the tracking problem to text instance association, and an appearancegeometry descriptor with memory mechanism is proposed to generate robust representation of text instances. By integrating these two branches into one trainable framework, they can promote each other and the computational cost is significantly reduced. Experiments on existing video text benchmarks including ICDAR2013 Video, Minetto and YVT demonstrate that the proposed method significantly outperforms stateoftheart methods. Our method improves Fscore by about 2% on all datasets and it can run realtime with 24.36 fps on TITAN Xp.

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.