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Detection and Recognition of Text Embedded in Online Images via Neural Context Models

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Authors
Kang, Chulmoo; Kim, Gunhee; Yoo, Suk I.
Issue Date
2017-02-07
Publisher
THE AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
Citation
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp. 4103-4110
Keywords
Text DetectionText RecognitionNeural NetworksContext Model
Abstract
We address the problem of detecting and recognizing the text embedded in online images that are circulated over the Web. Our idea is to leverage context information for both text detection and recognition. For detection, we use local image context around the text region, based on that the text often sequentially appear in online images. For recognition, we exploit the metadata associated with the input online image, including tags, comments, and title, which are used as a topic prior for the word candidates in the image. To infuse such two sets of context information, we propose a contextual text spotting network (CTSN). We perform comparative evaluation with five state-of-the-art text spotting methods on newly collected Instagram and Flickr datasets. We show that our approach that benefits from context information is more successful for text spotting in online images.
ISSN
2159-5399
Language
English
URI
https://hdl.handle.net/10371/116866
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Computer Science and Engineering (컴퓨터공학부)Journal Papers (저널논문_컴퓨터공학부)
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