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- W2339589954 abstract "In this paper, we propose a novel approach for text detec- tion in natural images. Both local and global cues are taken into account for localizing text lines in a coarse-to-fine pro- cedure. First, a Fully Convolutional Network (FCN) model is trained to predict the salient map of text regions in a holistic manner. Then, text line hypotheses are estimated by combining the salient map and character components. Fi- nally, another FCN classifier is used to predict the centroid of each character, in order to remove the false hypotheses. The framework is general for handling text in multiple ori- entations, languages and fonts. The proposed method con- sistently achieves the state-of-the-art performance on three text detection benchmarks: MSRA-TD500, ICDAR2015 and ICDAR2013." @default.
- W2339589954 created "2016-06-24" @default.
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- W2339589954 date "2016-06-01" @default.
- W2339589954 modified "2023-10-16" @default.
- W2339589954 title "Multi-oriented Text Detection with Fully Convolutional Networks" @default.
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- W2339589954 doi "https://doi.org/10.1109/cvpr.2016.451" @default.
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