2008 IEEE 16th Signal Processing, Communication and Applications Conference 2008
DOI: 10.1109/siu.2008.4632711
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Sliding text recognition in broadcast news

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Cited by 8 publications
(10 citation statements)
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“…To exemplify, Dikici and Saraclar presents a method that uses linguistic cues to correct sliding texts that are presented in newscasts, and as a result, word and character recognition is increased remarkably 9 . This study is a good example identifying the importance of the knowledge-based approach in improving the quality of the results.…”
Section: Literaturementioning
confidence: 99%
“…To exemplify, Dikici and Saraclar presents a method that uses linguistic cues to correct sliding texts that are presented in newscasts, and as a result, word and character recognition is increased remarkably 9 . This study is a good example identifying the importance of the knowledge-based approach in improving the quality of the results.…”
Section: Literaturementioning
confidence: 99%
“…The videos are given as input to a news story segmenter which is a silence detection implementation outputting the segment boundaries corresponding to news stories. These individual segments are fed into the sliding text recognizer presented in [8] and the resulting segment texts are provided as input to the rulebased named entity recognizer [9]. The named entities output by this recognizer constitute the semantic annotations of the news story segments.…”
Section: A News Video Databasementioning
confidence: 99%
“…In order to obtain the speech transcriptions of the videos which are also given as sliding texts in the videos of our data set, we employ the sliding text recognizer presented in [8]. This recognizer first converts the video frames into binary images and utilizes the horizontal and vertical histograms of these images to determine the exact text band.…”
Section: Sliding Text Recognitionmentioning
confidence: 99%
See 1 more Smart Citation
“…[3] proposed multi-frame combination technique as a robust video text feature extraction method. [4], [5] finds the text location from the horizontal projection of the grey-level image.…”
Section: Introductionmentioning
confidence: 99%