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At present, multi-oriented text detection methods based on deep neuralnetwork have achieved promising performances on various benchmarks.Nevertheless, there are still some difficulties for arbitrary shape textdetection, especially for a simple and proper representation of arbitrary shapetext instances. In this paper, a pixel-based text detector is proposed tofacilitate the representation and prediction of text instances with arbitraryshapes in a simple manner. Firstly, to alleviate the effect of the targetvertex sorting and achieve the direct regression of arbitrary shape textinstances, the starting-point-independent coordinates regression loss isproposed. Furthermore, to predict more accurate text instances, the textinstance accuracy loss is proposed as an assistant task to refine the predictedcoordinates under the guidance of IoU. To evaluate the effectiveness of ourdetector, extensive experiments have been carried on public benchmarks whichcontain arbitrary shape text instances and multi-oriented text instances. Weobtain 84.8% of F-measure on Total-Text benchmark. The results show that ourmethod can reach state-of-the-art performance.

Learning to Predict More Accurate Text Instances for Scene Text Detection