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CDText: Scene text detector based on context-aware deformable transformer
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AI
DOI：10.1016/j.patrec.2023.05.025.png)
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摘要
En 中文
Scene text detection task aims to precisely locate text regions in natural scenes. However, the existing methods still face challenges in detecting arbitrary-shaped text, due to their limited feature representation capability. To alleviate this problem, we propose a scene text detector, i.e., CDText, based on structure of context-aware deformable transformer. Specifically, CDText firstly adopts different convolution kernel designs for feature extraction, which designs receptive fields with different size for multi-scale feature perception and fusion. Meanwhile, multi-head self-attention mechanism is used to strengthen the reasoning ability of CDText in a global sense, thus enhancing feature maps with abundant context information by extracting implicit relationship between multi-scale text features. Moreover, CDText designs a segmentation head to segment text instances of arbitrary shapes from rectangular detection boxes. Experiments show that CDText is superior to comparative methods in detection accuracy, achieving F -scores of 92.7, 81.9, and 82.9 on ICDAR2013, Total Text, and CTW-150 0 datasets, respectively.
Keyword:
Disentangled representation learning
Group -disentangled feature representation
Thoracic pathologic prediction
期刊
IF:
3.3
论文数:
8.0K
被引数:
1.6W
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PREAI
分享收藏分享收藏Curved scene text detection via transverse and longitudinal sequence connection.png)
PATTERN RECOGNITION
OAAI
PATTERN RECOGNITIONIF7.6
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