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Using data mining for digital ink recognition: dividing text and shapes in sketched diagrams

Blagojevic, Rachel, Plimmer, Beryl, Grundy, John and Wang, Yong 2011, Using data mining for digital ink recognition: dividing text and shapes in sketched diagrams, Computers and graphics, vol. 35, no. 5, pp. 976-991, doi: 10.1016/j.cag.2011.07.002.

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Title Using data mining for digital ink recognition: dividing text and shapes in sketched diagrams
Author(s) Blagojevic, Rachel
Plimmer, Beryl
Grundy, JohnORCID iD for Grundy, John orcid.org/0000-0003-4928-7076
Wang, Yong
Journal name Computers and graphics
Volume number 35
Issue number 5
Start page 976
End page 991
Total pages 16
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2011-10
ISSN 0097-8493
Keyword(s) Science & Technology
Technology
Computer Science, Software Engineering
Computer Science
Sketch tools
Recognition algorithms
Sketch recognition
Pen-based interfaces
Summary The low accuracy rates of textshape dividers for digital ink diagrams are hindering their use in real world applications. While recognition of handwriting is well advanced and there have been many recognition approaches proposed for hand drawn sketches, there has been less attention on the division of text and drawing ink. Feature based recognition is a common approach for textshape division. However, the choice of features and algorithms are critical to the success of the recognition. We propose the use of data mining techniques to build more accurate textshape dividers. A comparative study is used to systematically identify the algorithms best suited for the specific problem. We have generated dividers using data mining with diagrams from three domains and a comprehensive ink feature library. The extensive evaluation on diagrams from six different domains has shown that our resulting dividers, using LADTree and LogitBoost, are significantly more accurate than three existing dividers.
Language eng
DOI 10.1016/j.cag.2011.07.002
Field of Research 080309 Software Engineering
Socio Economic Objective 890202 Application Tools and System Utilities
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2011, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30081850

Document type: Journal Article
Collection: School of Information Technology
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