EI2SR: Learning an Enhanced Intra-Instance Semantic Relationship for Arbitrary-Shaped Scene Text Detection

Abstract

Text detection in natural scenarios, has made significant progress with the deep learning architecture. Towards arbitrary-shaped text detection, fracture detection is the major concern due to the lack of semantic relationship within an instance in existing methods. To circumvent this dilemma, we propose a novel network to learn an Enhanced Intra-Instance Semantic Relationship (EI<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>SR) which consists of Text-Specific Attention Mechanism (TAM) and Border Attraction Grouping (BAG). The former models the rich semantic information between different coarse-grained text regions to guide the fine-grained learning of corresponding text representations. The latter enhances the border-center semantic correlation by establishing high-dimension embedding space to attract and group the border at both ends to their corresponding center. Extensive experimental results show that the proposed EI<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>SR achieves state-of-the-art or competitive performance on existing benchmarks.