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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 1, April 2006
  4. Retrieving poorly degraded OCR 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
International Journal of Document Analysis and Recognition (IJDAR) : Volume 8, Issue 1, April 2006
Retrieving poorly degraded OCR documents
Stable methods for recognizing acronym-expansion pairs: from rule sets to hidden Markov models
An MLP-orthogonal Gaussian mixture model hybrid model for Chinese bank check printed numeral recognition
Confidence modeling for handwriting recognition: algorithms and applications
On foreground — background separation in low quality document images
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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Retrieving poorly degraded OCR documents

Content Provider Springer Nature Link
Author Fataicha, Y. Cheriet, M. Nie, J. Y. Suen, C. Y.
Copyright Year 2005
Abstract A significant portion of currently available documents exist in the form of images, for instance, as scanned documents. Electronic documents produced by scanning and OCR software contain recognition errors. This paper uses an automatic approach to examine the selection and the effectiveness of searching techniques for possible erroneous terms for query expansion. The proposed method consists of two basic steps. In the first step, confused characters in erroneous words are located and editing operations are applied to create a collection of erroneous error-grams in the basic unit of the model. The second step uses query terms and error-grams to generate additional query terms, identify appropriate matching terms, and determine the degree of relevance of retrieved document images to the user's query, based on a vector space IR model. The proposed approach has been trained on 979 document images to construct about 2,822 error-grams and tested on 100 scanned Web pages, 200 advertisements and manuals, and 700 degraded images. The performance of our method is evaluated experimentally by determining retrieval effectiveness with respect to recall and precision. The results obtained show its effectiveness and indicate an improvement over standard methods such as vectorial systems without expanded query and 3-gram overlapping.
Starting Page 1
Ending Page 99999
Page Count 99999
File Format PDF
ISSN 14332833
Journal International Journal of Document Analysis and Recognition (IJDAR)
Volume Number 8
Issue Number 1
e-ISSN 14332825
Language English
Publisher Springer-Verlag
Publisher Date 2005-10-13
Publisher Place Berlin, Heidelberg
Access Restriction One Nation One Subscription (ONOS)
Content Type Text
Resource Type Article
Subject Computer Science Applications Computer Vision and Pattern Recognition Software
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