An efficient pre-processing method to identify logical components from PDF documents

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As the rapid growth of the scientific documents in digital libraries, the search demands for the documents as well as specific components increase dramatically. Accurately detecting the component boundary is of vital importance to all the further information extraction and applications. However, document component boundary detection (especially the table, figure, and equation) is a challenging problem because there is no standardized formats and layouts across diverse documents. This paper presents an efficient document preprocessing technique to improve the document component boundary detection performance by taking advantage of the nature of document lines. Our method easily simplifies the component boundary detection problem into the sparse line analysis problem with much less noise. We define eight document line label types and apply machine learning techniques as well as the heuristic rule-based method on identifying multiple document components. Combining with different heuristic rules, we extract the multiple components in a batch way by filtering out massive noises as early as possible. Our method focus on an important un-tagged document format - PDF documents. The experimental results prove the effectiveness of the sparse line analysis. © 2011 Springer-Verlag.
Publisher
Springer Verlag
Issue Date
2011
Language
English
Citation

LECTURE NOTES IN COMPUTER SCIENCE (INCLUDING SUBSERIES LECTURE NOTES IN ARTIFICIAL INTELLIGENCE AND LECTURE NOTES IN BIOINFORMATICS), v.6634 LNAI, no.PART 1, pp.500 - 511

ISSN
0302-9743
URI
http://hdl.handle.net/10203/99775
Appears in Collection
KSE-Journal Papers(저널논문)
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