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A spatial feature adaptive network for text detection
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DOI：10.1007/s11042-022-12619-3.png)
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摘要
En 中文
Due to the capacity of detection arbitrary shapes of text and the robustness in practical applications, scene text detection methods based on segmentation have attached more attention. More accurate segmentation and better feature extraction are the core of segmentation-based detection. In order to refine the result of segmentation, we replace the convolution in the first block of the ResNet50 by desubpixel convolution to enhance the feature extraction capabilities of the network. We also propose a spatial adaptive convolutional network to adjust the features extracted by the backbone so that the network can extract features more suitable for natural scene text detection. We implement the presented network based on PSENet. The results on ICDAR2015 and SCUT-CTW1500 demonstrate that our module can improve the performance of text detection. The precision, recall and F-measure have reached 87.27%, 84.88% and 86.06% on ICDAR2015. And they have reached 81.99%, 82.63% and 82.31% on CTW1500. Our code will be available at https://github.com/fengdashuai/Ada-PSENet.
Keyword:
Text detection
Desubpixel convolution
Convolution neural network
Spatial adaptive convolutional network
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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