Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICDAR.2007.4378764
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dc.titleExtraction of vectorized graphical information from scientific chart images
dc.contributor.authorLiu, R.
dc.contributor.authorHuang, W.
dc.contributor.authorChew, L.T.
dc.date.accessioned2013-07-04T08:07:20Z
dc.date.available2013-07-04T08:07:20Z
dc.date.issued2007
dc.identifier.citationLiu, R.,Huang, W.,Chew, L.T. (2007). Extraction of vectorized graphical information from scientific chart images. Proceedings of the International Conference on Document Analysis and Recognition, ICDAR 1 : 521-525. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/ICDAR.2007.4378764" target="_blank">https://doi.org/10.1109/ICDAR.2007.4378764</a>
dc.identifier.isbn0769528228
dc.identifier.issn15205363
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40568
dc.description.abstractGraphical components information extraction is a crucial step in the chart recognition and understanding process. However, existing methods of information extraction from chart images either are type-dependent or rely on certain assumptions. In this paper, we present a general method to extract vectorized graphical information from scientific chart images. Our algorithm firstly constructs a data structure called directional single-connected chains (DSCC). It then employs ellipse-specific fitting and orthogonal diagonalization to calculate the curvatures of the chains and classify the chains into either straight lines or arcs. Finally we combine all straight lines and all arcs accordingly and use linear regression to compute their attributes. The DSCC has a good property in that it is less susceptible to noise. The experiment results show that our algorithm is efficient, robust and accurate. © 2007 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICDAR.2007.4378764
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1109/ICDAR.2007.4378764
dc.description.sourcetitleProceedings of the International Conference on Document Analysis and Recognition, ICDAR
dc.description.volume1
dc.description.page521-525
dc.identifier.isiutNOT_IN_WOS
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