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- W2204328893 abstract "We propose a novel easy-to-implement stroke detector based on an efficient pixel intensity comparison to surrounding pixels. Stroke-specific keypoints are efficiently detected and text fragments are subsequently extracted by local thresholding guided by keypoint properties. Classification based on effectively calculated features then eliminates non-text regions. The stroke-specific keypoints produce 2 times less region segmentations and still detect 25% more characters than the commonly exploited MSER detector and the process is 4 times faster. After a novel efficient classification step, the number of regions is reduced to 7 times less than the standard method and is still almost 3 times faster. All stages of the proposed pipeline are scale-and rotation-invariant and support a wide variety of scripts (Latin, Hebrew, Chinese, etc.) and fonts. When the proposed detector is plugged into a scene text localization and recognition pipeline, a state-of-the-art text localization accuracy is maintained whilst the processing time is significantly reduced." @default.
- W2204328893 created "2016-06-24" @default.
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- W2204328893 date "2015-12-01" @default.
- W2204328893 modified "2023-10-17" @default.
- W2204328893 title "FASText: Efficient Unconstrained Scene Text Detector" @default.
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- W2204328893 doi "https://doi.org/10.1109/iccv.2015.143" @default.
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