Visualization results of text instances for our method DPNet and the baseline on different types of datasets.

Visualization results of text instances for our method DPNet and the baseline on different types of datasets.

<p>The images are randomly selected from three datasets, which better demonstrate the robustness of our model.</p>

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Bibliographic Details
Main Author:	Yuan Li (67017) (author)
Published:	2024
Subjects:	Microbiology Science Policy Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified spatial enhanced self integration effectively facilitates channel enhanced self automatically extracts features achieves performance improvements lacks global attributes feature decoder designed icdar 2015 dataset global contextual information dual perspective cnn scene text detection paper significantly improves deep learning algorithm transformer </ p global information text detection feature map evolved significantly deep learning text dataset td500 dataset detailed information xlink "> thereby alleviating recent years positional relationships paper introduces experiments show different types detecting text convolution operation continuous advancement computer vision complex backgrounds attention module 72 vs 07 vs
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Description
Summary:	<p>The images are randomly selected from three datasets, which better demonstrate the robustness of our model.</p>

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