Automatic text segmentation and text recognition for video indexing

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URL:	https://madoc.bib.uni-mannheim.de/781
URN:	urn:nbn:de:bsz:180-madoc-7810
Document Type:	Working paper
Year of publication:	1998
The title of a journal, publication series:	Technical Reports
Volume:	98-009
Place of publication:	Mannheim
Publication language:	English
Institution:	School of Business Informatics and Mathematics > Sonstige - Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik
MADOC publication series:	Veröffentlichungen der Fakultät für Mathematik und Informatik > Institut für Informatik > Technical Reports
Subject:	004 Computer science, internet
Subject headings (SWD):	Videobearbeitung , Indizierung <Informatik> , Optische Zeichenerkennung , Automatische Inhaltsanalyse
Individual keywords (German):	Videoverarbeitung , Automatische Inhaltsanalyse , Indizierung <Informatik> , Optische Zeichenerkennung
Keywords (English):	video processing , character segmentation , character recognition , OCR , video indexing , video content analysis
Abstract:	Efficient indexing and retrieval of digital video is an important function of video databases. One powerful index for retrieval is the text appearing in them. It enables content-based browsing. We present our methods for automatic seg-mentation of text in digital videos. The output is directly passed to a standard OCR software package in order to translate the segmented text into ASCII. The algorithms we propose make use of typical characteristics of text in videos in order to enable and enhance segmentation performance. Especially the inter-frame dependencies of the characters provide new possibilities for their refinement. Then, a straightforward indexing and retrieval scheme is intro-duced. It is used in the experiments to demonstrate that the proposed text segmentation algorithms together with exist-ing text recognition algorithms are suitable for indexing and retrieval of relevant video sequences in and from a video database. Our experimental results are very encouraging and suggest that these algorithms can be used in video retrieval applications as well as to recognize higher seman-tics in videos.