Visualization of the results of our method on different types of text instances, including multi-oriented, multi-lingual, and curved text.

Visualization of the results of our method on different types of text instances, including multi-oriented, multi-lingual, and curved text.

<p>The second to fourth rows correspond to the probability map, the threshold map, and the binarization map for each text instance in the images, respectively.</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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