Con-Text: Text Detection Using Background Connectivity for Fine-Grained Object Classification. Sezer Karaoglu, Jan van Gemert, Theo Gevers. Can we achieve a better object recognition with the help of scene-text ?. Goal.
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Con-Text: Text Detection Using Background Connectivity for Fine-Grained Object Classification
Sezer Karaoglu, Jan van Gemert, Theo Gevers
Can we achieve a better object recognition with the help of scene-text?
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Application : Linking images from Google street view to textual business inforation as e.g. the Yellow pages, Geo-referencing, Information retrieval
Wang et al. “End-to-End Scene Text Recognition”
Epshtein et al. “Detecting Text in Natural Scenes
with Stroke Width Transform ” CVPR ‘10
Text detection by BG
Automatic BG seed selection
where B is the structring element (3 by-3 square), M is the binary image where bg seeds are ones and X is the gray level input image
The proposed system removes 87% of the non-text regions where on average
91% of the test set contains non-text regions. It retains approximately %98 of text regions.
ocr : 15.6 ± 0.4
Bow + ocr : 39.0 ± 2.6
Bow : 32.9 ± 1.7
suitable approach for scene
using background connectivity
and, color, contrast and
objectness cues is proposed.
with visual and scene text