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We study the problem of extracting text instance contour information fromimages and use it to assist scene text detection. We propose a novel andeffective framework for this and experimentally demonstrate that: (1) A CNNthat can be effectively used to extract instance-level text contour fromnatural images. (2) The extracted contour information can be used for betterscene text detection. We propose two ways for learning the contour tasktogether with the scene text detection: (1) as an auxiliary task and (2) asmulti-task cascade. Extensive experiments with different benchmark datasetsdemonstrate that both designs improve the performance of a state-of-the-artscene text detector and that a multi-task cascade design achieves the bestperformance.

TextContourNet: a Flexible and Effective Framework for Improving Scene Text Detection Architecture with a Multi-task Cascade