Scene Text Detection Algorithm Based on Enhanced Feature Pyramid Network

Abstract

Scene text detection helps machines understand image content,and is widely used in the fields such as intelligent transportation,scene understanding,and intelligent navigation.Existing scene text detection algorithms do not make full use of high-level semantic information and spatial information,which limits the model's ability to classify complex background pixels and the ability to detect and locate text instances of different scales.In order to solve the above problems,a scene text detection algorithm based on enhanced feature pyramid network is proposed.The algorithm includes a RIFE (ratio invariant feature enhanced) mo-dule and a RSR (rebuild spatial resolution) module.As the residual branch,the RIFE module enhances the high-level semantic information transmission of the network,improves the classification ability,and reduces the false positive rate and the false negative rate.The RSR module reconstructs multi-layer feature resolution and uses rich spatial information to improve the boundary location.Experimental results show that the proposed algorithm improves the detection capabilities on the multi-directional text dataset ICDAR2015,the curved text dataset Totaltext,and the long text dataset MSRA-TD500.

Keywords

• scene text detection|feature pyramid network|semantic information|spatial information|boundary location