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- W2016249943 abstract "Several powerful approaches have recently been proposed for writer identification, which rely on local descriptors that capture the texture, shape and curvature properties of the handwriting. In this paper we use combinations of three of these features (K-Adjacent Segments, SURF, and Contour Gradient Descriptors), to address the writer identification problem. Experiments demonstrate that feature combinations outperform individual features, resulting in state-of-the-art performance on three datasets." @default.
- W2016249943 created "2016-06-24" @default.
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- W2016249943 date "2014-09-01" @default.
- W2016249943 modified "2023-10-17" @default.
- W2016249943 title "Combining Local Features for Offline Writer Identification" @default.
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- W2016249943 doi "https://doi.org/10.1109/icfhr.2014.103" @default.
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