Enhanced EAST: Improving Networks Feature Extraction Ability and Text Complete Shape Perception EAST 1 is a popular end to end text detector, however, it performs deficiently with long or large texts, because of limited networku0027s feature extraction capacity and narrow receptive fields. Based on EAST, we propose an approach named Enhanced EAST. Firstly, we offer lowlevel feature layers more semantic information by introducing information from high levels to low levels, which reduces the information gap between different layers. Meanwhile, we utilize a twostream large kernel convolution to increase receptive fields with reasonable computational cost, therefore, improving the networku0027s features detection and fusion ability. In addition, we also optimize the label generation of training data and design a weighted mask for each text, which can guide the training process to enhance the networku0027s complete shape perception of texts, thus impelling the predicted text boxes locate more accurately. In the end, we perform data equalization and augmentation in the experiments and experiment results on ICDAR 2015, MSRATD500 and ICDAR 2017 MLT datasets demonstrate the proposed algorithm achieves a stateofart performance in multioriented scene text detection.

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.