The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
An approach to the segmentation of multi-page document flow using binary classification
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Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Research works printed but not linked: 8
- A. Gordo, M. Rusinol, D. Karatzas,and A. D. Bagdanov, “Document Classification and Page Stream Segmentation for Digital Mailroom Applications,” 12th Conference on Document Analysis and Recognition (ICDAR), 621–625, (2013). · DOI · Paper page
- M. Schmidtler and J. Amtrup, “Automatic document separation: A combination of probabilistic classification and finite-state sequence modelling,” in Natural Language Processing and Tex. Mining, Springer-Verlag. London, 123–144, (2006).
- K. Collins-Thompson and R. Nickolov, “A clustering-based algorithm for automatic document separation,” in SIGIR 2002 Workshop on Information Retrieval and OCR: From Converting Content to Graspin. Meaning,(2002).
- H. Daher and A. Belaid, “Document Flow Segmentation for Business Applications,” in Document Recognition and Retrieva. XXI, Sa. Francisco, USA, (2014). · DOI
- G. Csurka, C. R. Dance, F. Fan, F. Willamowski, and C. Bray, “Visual categorization with bag of keypoints,” in Workshop on Statistical Learning in Computer Vision (ECCV), 1–22, (2004).
- J. Winn, A. Criminisi, and T. Minka, “Object categorization by learned universal visual dictionary,” i. ICCV, 1800–1807, (2005). · DOI
- 10.1145/1961189 · DOI
- 10.1016/j.ipm.2009.03.002 · DOI · Paper page
Metadata
- Authors
- Agin, O., Ulas, C., Ahat, M., & Bekar, C.
- Venue
- SPIE Proceedings
- Publisher
- SPIE
- Published
- 2015-03-04
- Volume
- 9443
- Pages
- 944311
- ISSN
- 0277-786X
- Cited by
- 2
- References
- 1
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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