Swin Transformer Based Text Super-Resolution Method
Text images in natural scenes often have complex and diverse backgrounds.Due to the limitations of shoot-ing conditions,these images may have issues such as blurring,dimming,or low resolution,which poses challenges to text recognition tasks.In order to improve the clarity of text regions and improve the accuracy of text recognition,this paper proposes a text super-resolution network TSSRN.This algorithm introduces Swin Transformer on the basis of text super-resolution network TBSRN to perceive more levels of feature information.In addition,the edge Loss function is introduced to enhance texture structure features.Finally,this paper conducted training and testing on the TextZoom dataset,and the ex-perimental results showed that the method has achieved certain improvements in super-resolution reconstruction of text im-ages,which is also improved compared to the original TBSRN algorithm,thus verifying the effectiveness of the algorithm.
万方数据
维普
