A Heuristic Approach to Caption Enhancement for Effective Video OCR
Identifieur interne : 000331 ( Istex/Curation ); précédent : 000330; suivant : 000332A Heuristic Approach to Caption Enhancement for Effective Video OCR
Auteurs : Lei Xie [République populaire de Chine] ; Xi Tan [République populaire de Chine]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2008.
Abstract
Abstract: We present a heuristic approach to enhancing speech synchronized captions for video OCR, as a pre-process for subsequent tasks of multimedia indexing, segmentation and retrieval. We use a bi-search based caption transition detection method to improve efficiency, which adopts a simple heuristics that the same caption content usually lasts for a period for stable viewing. We propose a combination of color mask, changing mask and region mask to perform caption enhancement based on the discriminative characteristics of captions and backgrounds. Elaborate enhancement on individual characters is further used to remove small background residues. OCR experiments show that our caption enhancement approach brings a high character accuracy of 89.24%.
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DOI: 10.1007/978-3-540-87442-3_44
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Lei Xie<affiliation><mods:affiliation>Human-Computer Communications Laboratory, The Chinese University of Hong Kong, Hong Kong SAR</mods:affiliation><wicri:noCountry code="subField">SAR</wicri:noCountry></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: lxie@nwpu.edu.cn</mods:affiliation><country wicri:rule="url">République populaire de Chine</country></affiliation>Le document en format XML
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We use a bi-search based caption transition detection method to improve efficiency, which adopts a simple heuristics that the same caption content usually lasts for a period for stable viewing. We propose a combination of color mask, changing mask and region mask to perform caption enhancement based on the discriminative characteristics of captions and backgrounds. Elaborate enhancement on individual characters is further used to remove small background residues. OCR experiments show that our caption enhancement approach brings a high character accuracy of 89.24%.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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