&lt;title&gt;Table structure recognition based on robust block segmentation&lt;/title&gt;

Kieninger, Thomas

doi:10.1117/12.304642

Cited by 93 publications

(50 citation statements)

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“…Pyreddy and Croft demonstrated results on over 6000 tables from the Wall Street Journal [18]. T-Recs clustered words for bottom-up structural analysis of ASCII tables [19]. Hu et al explored row and column alignment via directed acyclic attribute graphs [20].…”

Section: Physical Structure Extractionmentioning

confidence: 99%

Converting heterogeneous statistical tables on the web to searchable databases

Embley

Krishnamoorthy

Nagy

et al. 2016

IJDAR

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Much of the world's quantitative data resides in scattered web tables. For a meaningful role in Big Data analytics, the facts reported in these tables must be brought into a uniform framework. Based on a formalization of header-indexed tables, we proffer an algorithmic solution to end-to-end table processing for a large class of humanreadable tables. The proposed algorithms transform headerindexed tables to a category table format that maps easily to a variety of industry-standard data stores for query processing. The algorithms segment table regions based on the unique indexing of the data region by header paths, classify table cells, and factor header category structures of two-dimensional as well as the less common multidimensional tables. Experimental evaluations substantiate the algorithmic approach to processing heterogeneous tables. As demonstrable results, the algorithms generate queryable relational database tables and semantic-web triple stores. Application of our algorithms to 400 web tables randomly selected from diverse sources shows that the algorithmic solution automates end-to-end table processing. document analysis table segmentation table analysis table header factoring end-to-end table processing· table headers queries over table data ___________________________________________ Keywords

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Section: Physical Structure Extractionmentioning

confidence: 99%

Converting heterogeneous statistical tables on the web to searchable databases

Embley

Krishnamoorthy

Nagy

et al. 2016

IJDAR

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“…The method of literature [28], however, depends on the output of commercial OCR systems. On the other hand, our proposed method identifies table types, i.e.…”

Section: Related Workmentioning

confidence: 99%

“…As for related methods for document layout and structure recognition, literature [28] reports the table structure recognition method based on the block segmentation method and literature [29] tries to extract the contents from printed document images using model checking. The method of literature [28], however, depends on the output of commercial OCR systems.…”

Section: Related Workmentioning

confidence: 99%

Document Image Processing for Hospital Information Systems

Kawanaka¹,

Yamamoto²,

Takase³

et al. 2012

Modern Information Systems

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“…Pyreddy and Croft demonstrated results on over 6000 tables from the Wall Street Journal 20 . T-Recs clustered words for bottom-up structural analysis of ASCII tables 21 . Row and column alignment via directed acyclic attribute graphs was also explored 22 .…”

Section: Physical Structure Extraction (Low-level Table Processing)mentioning

confidence: 99%

Clustering header categories extracted from web tables

et al. 2015

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Revealing related content among heterogeneous web tables is part of our long term objective of formulating queries over multiple sources of information. Two hundred HTML tables from institutional web sites are segmented and each table cell is classified according to the fundamental indexing property of row and column headers. The categories that correspond to the multi-dimensional data cube view of a table are extracted by factoring the (often multi-row/column) headers. To reveal commonalities between tables from diverse sources, the Jaccard distances between pairs of category headers (and also table titles) are computed. We show how about one third of our heterogeneous collection can be clustered into a dozen groups that exhibit table-title and header similarities that can be exploited for queries.

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<title>Table structure recognition based on robust block segmentation</title>

Cited by 93 publications

References 0 publications

Converting heterogeneous statistical tables on the web to searchable databases

Converting heterogeneous statistical tables on the web to searchable databases

Document Image Processing for Hospital Information Systems

Clustering header categories extracted from web tables

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