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  1. International Journal of Document Analysis and Recognition (IJDAR)
  2. International Journal of Document Analysis and Recognition (IJDAR) : Volume 8
  3. International Journal of Document Analysis and Recognition (IJDAR) : Volume 8, Issue 2-3, June 2006
  4. Consensus-based table form recognition of low-quality historical documents
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International Journal of Document Analysis and Recognition (IJDAR) : Volume 20
International Journal of Document Analysis and Recognition (IJDAR) : Volume 19
International Journal of Document Analysis and Recognition (IJDAR) : Volume 18
International Journal of Document Analysis and Recognition (IJDAR) : Volume 17
International Journal of Document Analysis and Recognition (IJDAR) : Volume 16
International Journal of Document Analysis and Recognition (IJDAR) : Volume 15
International Journal of Document Analysis and Recognition (IJDAR) : Volume 14
International Journal of Document Analysis and Recognition (IJDAR) : Volume 13
International Journal of Document Analysis and Recognition (IJDAR) : Volume 12
International Journal of Document Analysis and Recognition (IJDAR) : Volume 11
International Journal of Document Analysis and Recognition (IJDAR) : Volume 10
International Journal of Document Analysis and Recognition (IJDAR) : Volume 9
International Journal of Document Analysis and Recognition (IJDAR) : Volume 8
International Journal of Document Analysis and Recognition (IJDAR) : Volume 8, Issue 4, September 2006
International Journal of Document Analysis and Recognition (IJDAR) : Volume 8, Issue 2-3, June 2006
Special issue on detection and understanding of tables and forms for document processing applications
Table-processing paradigms: a research survey
Fourier–Mellin registration of line-delineated tabular document images
DMOS, a generic document recognition method: application to table structure analysis in a general and in a specific way
Towards a theory of tables
Detection and analysis of table of contents based on content association
Design of an end-to-end method to extract information from tables
A simple and effective table detection system from document images
Consensus-based table form recognition of low-quality historical documents
Table form document analysis based on the document structure grammar
Generalization of Frei and Chen's approach to extract the lines and edges of a digital image
International Journal of Document Analysis and Recognition (IJDAR) : Volume 8, Issue 1, April 2006
International Journal of Document Analysis and Recognition (IJDAR) : Volume 7
International Journal of Document Analysis and Recognition (IJDAR) : Volume 6
International Journal of Document Analysis and Recognition (IJDAR) : Volume 5
International Journal of Document Analysis and Recognition (IJDAR) : Volume 4
International Journal of Document Analysis and Recognition (IJDAR) : Volume 3
International Journal of Document Analysis and Recognition (IJDAR) : Volume 2
International Journal of Document Analysis and Recognition (IJDAR) : Volume 1

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Consensus-based table form recognition of low-quality historical documents

Content Provider Springer Nature Link
Author Nielson, H. Barrett, W.
Copyright Year 2005
Abstract Given a set of low-quality line-delimited tabular documents of the same layout, we present a robust zoning algorithm which exploits both intra- and inter-document consensus to extract the structure of the table. The structure is captured in the form of a document template, that can then be snapped to a new document to perform automated “cookie cutter” data extraction. We also report a companion consensus-based algorithm for the classification of zone content as either machine print, handwriting or empty. Using scanned Census records from 1841 to 1881, the template is recovered with an efficiency of.076 [0, 1). Using consensus over about 10 documents from each data set, this error was reduced to.0076, or by 90%, which amounts to two missing line segments and one false positive. Similarly, the error for coverage was reduced from 0.098 to 0.016, or by 83%. Use of consensus also resulted in machine print classification accuracy of 100% for two of the three data sets. The classification error for handwriting averaged 0.1225 per document. By exploiting consensus within and between documents, automated zoning and labeling is greatly improved, providing field-level indexing of document content.
Starting Page 183
Ending Page 200
Page Count 18
File Format PDF
ISSN 14332833
Journal International Journal of Document Analysis and Recognition (IJDAR)
Volume Number 8
Issue Number 2-3
e-ISSN 14332825
Language English
Publisher Springer-Verlag
Publisher Date 2006-02-07
Publisher Place Berlin, Heidelberg
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Zoning Table recognition
Content Type Text
Resource Type Article
Subject Computer Science Applications Computer Vision and Pattern Recognition Software
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