Scene Text Detection Algorithm Based on Feature Enhancement
To address the problem of missed and false detection of image text in natural scenes due to complex backgrounds and variable scales,this paper proposes a text detection algorithm for scenes based on feature enhancement.In the feature pyramid fu-sion stage,a dual-domain attention feature fusion module(D2AAFM)is proposed,which can better fuse feature map information of different semantics and scales,thus improving the characterization ability of text information.At the same time,considering the problem of semantic information loss in the process of up-sampling and fusion of deeper feature maps of the network,the multi-scale spatial perception module(MSPM)is proposed to enhance the semantic features of text in higher-level feature maps by ex-panding the perceptual field to obtain contextual information of a larger perceptual field,thus effectively reduce the text of missed and false detection.In order to evaluate the effectiveness of the proposed algorithm,it is tested on the publicly available datasets ICDAR2015,CTW1500 and MSRA-TD500,and its overall index F-value reaches 82.8％,83.4％and 85.3％,respectively.The experimental results show that the algorithm has good detection capability on different datasets.
Deep learningScene text detectionAttention mechanismsMulti-scale feature fusionDilated convolution
NETL
NSTL
万方数据
