Learning to Group Text Lines and Regions in Freeform Handwritten Notes

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	Similar Articles Articles by M. Ye Articles by P. Viola Articles by S. Raghupathy Articles by H. Sutanto Articles by C. Li

Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 1

Curitiba, Parana, Brazil

September 23-September 26

ISBN: 0-7695-2822-8

	ASCII Text	x
	M. Ye, P. Viola, S. Raghupathy, H. Sutanto, C. Li, "Learning to Group Text Lines and Regions in Freeform Handwritten Notes," Document Analysis and Recognition, International Conference on, vol. 1, pp. 28-32, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 1, 2007.

	BibTex	x
	@article{ 10.1109/ICDAR.2007.159, author = {M. Ye and P. Viola and S. Raghupathy and H. Sutanto and C. Li}, title = {Learning to Group Text Lines and Regions in Freeform Handwritten Notes}, journal ={Document Analysis and Recognition, International Conference on}, volume = {1}, year = {2007}, issn = {1520-5363}, pages = {28-32}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDAR.2007.159}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Document Analysis and Recognition, International Conference on TI - Learning to Group Text Lines and Regions in Freeform Handwritten Notes SN - 1520-5363 SP28 EP32 A1 - M. Ye, A1 - P. Viola, A1 - S. Raghupathy, A1 - H. Sutanto, A1 - C. Li, PY - 2007 VL - 1 JA - Document Analysis and Recognition, International Conference on ER -

M. Ye, Microsoft Corporation, One Microsoft Way, Redmond, WA

P. Viola, Microsoft Corporation, One Microsoft Way, Redmond, WA

S. Raghupathy, Microsoft Corporation, One Microsoft Way, Redmond, WA

H. Sutanto, Microsoft Corporation, One Microsoft Way, Redmond, WA

C. Li, Microsoft Corporation, One Microsoft Way, Redmond, WA

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDAR.2007.159

This paper proposes a machine learning approach to grouping problems in ink parsing. Starting from an initial segmentation, hypotheses are generated by perturbing lo- cal configurations and processed in a high-confidence-first fashion, where the confidence of each hypothesis is pro- duced by a data-driven AdaBoost decision-tree classifier with a set of intuitive features. This framework has success- fully applied to grouping text lines and regions in complex freeform digital ink notes from real TabletPC users. It holds great potential in solving many other grouping problems in the ink parsing and document image analysis domains.

Citation:

M. Ye, P. Viola, S. Raghupathy, H. Sutanto, C. Li, "Learning to Group Text Lines and Regions in Freeform Handwritten Notes," icdar, vol. 1, pp.28-32, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 1, 2007

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