EI2SR: Learning an Enhanced Intra-Instance Semantic Relationship for Arbitrary-Shaped Scene Text Detection
Yan Shu, Shaohui Liu, Yu Zhou, Honglei Xu, Feng Jiang · 2023
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 (EI2SR) 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 EI2SR achieves state-of-the-art or competitive performance on existing benchmarks.