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• W2126123918 abstract "We address the problem of text localization and retrieval in real world images. We are first to study the retrieval of text images, i.e. the selection of images containing text in large collections at high speed. We propose a novel representation, textual visual words, which describe text by generic visual words that geometrically consistently predict bottom and top lines of text. The visual words are discretized SIFT descriptors of Hessian features. The features may correspond to various structures present in the text - character fragments, individual characters or their arrangements. The textual words representation is invariant to affine transformation of the image and local linear change of intensity. Experiments demonstrate that the proposed method outperforms the state-of-the-art on the MS dataset. The proposed method detects blurry, small font, low contrast, noisy text from real world images." @default.
• W2126123918 created "2016-06-24" @default.
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• W2126123918 date "2015-08-01" @default.
• W2126123918 modified "2023-10-17" @default.
• W2126123918 title "Towards visual words to words" @default.
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• W2126123918 doi "https://doi.org/10.1109/icdar.2015.7333840" @default.
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