Label distribution learning for scene text detection |
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Authors: | Haoyu MA Ningning LU Junjun MEI Tao GUAN Yu ZHANG Xin GENG |
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Affiliation: | 1. School of Computer Science and Engineering, and the Key Lab of Computer Network and Information Integration (Ministry of Education), Southeast University, Nanjing 211189, China2. ZTE Corporation, Nanjing 210012, China |
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Abstract: | Recently, segmentation-based scene text detection has drawn a wide research interest due to its flexibility in describing scene text instance of arbitrary shapes such as curved texts. However, existing methods usually need complex post-processing stages to process ambiguous labels, i.e., the labels of the pixels near the text boundary, which may belong to the text or background. In this paper, we present a framework for segmentation-based scene text detection by learning from ambiguous labels. We use the label distribution learning method to process the label ambiguity of text annotation, which achieves a good performance without using additional post-processing stage. Experiments on benchmark datasets demonstrate that our method produces better results than state-of-the-art methods for segmentation-based scene text detection. |
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Keywords: | scene text detection multi-task learning label distribution learning |
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