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A system for understanding imaged infographics and its applications
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Document Engineering archive
Proceedings of the 2007 ACM symposium on Document engineering table of contents
Winnipeg, Manitoba, Canada
SESSION: Paper documents: capture and physical-digital-coexitence table of contents
Pages: 9 - 18  
Year of Publication: 2007
ISBN:978-1-59593-776-6
Authors
Weihua Huang  National University of Singapore
Chew Lim Tan  National University of Singapore
Sponsors
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Information graphics, or infographics, are visual representations of information, data or knowledge. Understanding of infographics in documents is a relatively new research problem, which becomes more challenging when infographics appear as raster images. This paper describes technical details and practical applications of the system we built for recognizing and understanding imaged infographics located in document pages. To recognize infographics in raster form, both graphical symbol extraction and text recognition need to be performed. The two kinds of information are then auto-associated to capture and store the semantic information carried by the infographics. Two practical applications of the system are introduced in this paper, including supplement to traditional optical character recognition (OCR) system and providing enriched information for question answering (QA). To test the performance of our system, we conducted experiments using a collection of downloaded and scanned infographic images. Another set of scanned document pages from the University of Washington document image database were used to demonstrate how the system output can be used by other applications. The results obtained confirm the practical value of the system.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Weihua Huang: colleagues
Chew Lim Tan: colleagues