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A framework for improved video text detection and recognition

  • Text displayed in a video is an essential part for the high-level semantic information of the video content. Therefore, video text can be used as a valuable source for automated video indexing in digital video libraries. In this paper, we propose a workflow for video text detection and recognition. In the text detection stage, we have developed a fast localization-verification scheme, in which an edge-based multi-scale text detector first identifies potential text candidates with high recall rate. Then, detected candidate text lines are refined by using an image entropy-based filter. Finally, Stroke Width Transform (SWT)- and Support Vector Machine (SVM)-based verification procedures are applied to eliminate the false alarms. For text recognition, we have developed a novel skeleton-based binarization method in order to separate text from complex backgrounds to make it processible for standard OCR (Optical Character Recognition) software. Operability and accuracy of proposed text detection and binarization methods have been evaluatedText displayed in a video is an essential part for the high-level semantic information of the video content. Therefore, video text can be used as a valuable source for automated video indexing in digital video libraries. In this paper, we propose a workflow for video text detection and recognition. In the text detection stage, we have developed a fast localization-verification scheme, in which an edge-based multi-scale text detector first identifies potential text candidates with high recall rate. Then, detected candidate text lines are refined by using an image entropy-based filter. Finally, Stroke Width Transform (SWT)- and Support Vector Machine (SVM)-based verification procedures are applied to eliminate the false alarms. For text recognition, we have developed a novel skeleton-based binarization method in order to separate text from complex backgrounds to make it processible for standard OCR (Optical Character Recognition) software. Operability and accuracy of proposed text detection and binarization methods have been evaluated by using publicly available test data sets.show moreshow less

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Metadaten
Author details:Haojin YangGND, Bernhard Quehl, Harald SackORCiDGND
DOI:https://doi.org/10.1007/s11042-012-1250-6
ISSN:1380-7501
ISSN:1573-7721
Title of parent work (English):Multimedia tools and applications : an international journal
Publisher:Springer
Place of publishing:Dordrecht
Publication type:Article
Language:English
Year of first publication:2014
Publication year:2014
Release date:2017/03/27
Tag:Multimedia retrieval; Video OCR; Video indexing
Volume:69
Issue:1
Number of pages:29
First page:217
Last Page:245
Funding institution:Mediaglobe project; German Federal Ministry of Economics and Technology on the basis of a decision by the German Bundestag [FKZ: 01MQ09031]
Organizational units:An-Institute / Hasso-Plattner-Institut für Digital Engineering gGmbH
Peer review:Referiert
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