Natural Scene Text Detection Algorithm Based on Multi-branch Feature Fusion
EAST is an efficient and accurate scene text detection algorithm,but due to the limitation of receptive field,it is prone to false detection and missed detection when detecting small text,and lacks certain integrity when detecting long text.Aiming at the above problems,a natural scene text detection algorithm based on multi-branch feature fusion is proposed.Based on the EAST,the proposed al-gorithm introduces and improves the shallow feature enhancement module(RFB-s),and increases the receptive field of the shallow network to improve the problem of insufficient semantic information of shallow features on the premise of avoiding the loss of small text information to enhance the accuracy of small text positioning.The Recurrent Criss-Cross Attention Module(RCCAM)is introduced and improved,so that each pixel in the feature map can capture the contextual information of the full image in a very effective way,improving the detection ability of long text.At the same time,for the regression task,Dice Loss is used as the loss function to solve the problem of unbalanced proportion of positive and negative samples.EIoU is used to improve the effect of regression and get a more accurate text box.The proposed algorithm was tested on the ICDAR2015 and MSRA-TD500 datasets,and both achieved good detection results.It is showed that the proposed algorithm can effectively detect natural scene text and improve the accuracy of detection.
text detectionEASTshallow feature enhancementrecurrent criss-cross attentionloss function
万方数据
维普
