This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Table Detection in Online Ink Notes
August 2006 (vol. 28 no. 8)
pp. 1341-1346
ASCII Text
x 
 
Zhouchen Lin, Junfeng He, Zhicheng Zhong, Rongrong Wang, Heung-Yeung Shum, "Table Detection in Online Ink Notes," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 8, pp. 1341-1346, August, 2006.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2006.173,
author = {Zhouchen Lin and Junfeng He and Zhicheng Zhong and Rongrong Wang and Heung-Yeung Shum},
title = {Table Detection in Online Ink Notes},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {28},
number = {8},
issn = {0162-8828},
year = {2006},
pages = {1341-1346},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.173},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Table Detection in Online Ink Notes
IS - 8
SN - 0162-8828
SP1341
EP1346
EPD - 1341-1346
A1 - Zhouchen Lin,
A1 - Junfeng He,
A1 - Zhicheng Zhong,
A1 - Rongrong Wang,
A1 - Heung-Yeung Shum,
PY - 2006
KW - Table detection
KW - table recognition
KW - graphics recognition
KW - handwriting recognition
KW - document analysis
KW - pen-based computing.
VL - 28
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
In documents, tables are important structured objects that present statistical and relational information. In this paper, we present a robust system which is capable of detecting tables from free style online ink notes and extracting their structure so that they can be further edited in multiple ways. First, the primitive structure of tables, i.e., candidates for ruling lines and table bounding boxes, are detected among drawing strokes. Second, the logical structure of tables is determined by normalizing the table skeletons, identifying the skeleton structure, and extracting the cell contents. The detection process is similar to a decision tree so that invalid candidates can be ruled out quickly. Experimental results suggest that our system is robust and accurate in dealing with tables having complex structure or drawn under complex situations.

[1] G. Nagy, “Twenty Years of Document Image Analysis in PAMI,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 38-62, Jan. 2000.
[2] J.C. Handley, “Document Recognition,” Electronic Imaging Technology, E.R. Dougherty, ed., Bellingham, Wash.:IS & T/SPIE Optical Eng., chapter 8, pp. 289-316, 1999.
[3] D. Lopresti and G. Nagy, “A Tabular Survey of Automated Table Processing,” Proc. Third Int'l Workshop Graphics Recognition, Recent Advances, pp. 93-120, 1999.
[4] R. Zanibbi, D. Blostein, and J.R. Cordy, “A Survey of Table Recognition: Models, Observations, Transformations, and Inferences,” Int'l J. Document Analysis and Recognition, vol. 7, no. 1, pp. 1-16, 2004.
[5] D. Lopresti and G. Nagy, “Automated Table Processing: An (Opinionated) Survey,” Proc. Third Int'l Workshop Graphics Recognition, pp. 109-134, 1999.
[6] J.H. Shamilian, H.S. Baird, and T.L. Wood, “A Retargetable Table Reader,” Proc. IEEE Int'l Conf. Document Analysis and Recognition, pp. 158-163, 1997.
[7] E. Green and M. Krishnamoorthy, “Model-Based Analysis of Printed Tables,” Proc. IEEE Int'l Conf. Document Analysis and Recognition, pp. 214-217, 1999.
[8] T.G. Kieninger, “Table Structure Recognition Based on Robust Block Segmentation,” Proc. Fifth SPIE Conf. Document Recognition, pp. 22-32, 1998.
[9] A.K. Jain, A. Namboodiri, and J. Subrahmonia, “Structure in On-Line Documents,” Proc. IEEE Int'l Conf. Document Analysis and Recognition, pp. 844-848, 2001.
[10] A. Laurentini and P. Viada, “Identifying and Understanding Tabular Material in Compound Documents,” Proc. 11th Int'l Conf. Pattern Recognition, pp. 405-409, 1992.
[11] J. Sklansky and V. Gonzalez, “Fast Polygonal Approximation of Digitized Curves,” Pattern Recognition, vol. 12, pp. 327-331, 1980.
[12] Y. Wang, I.T. Phillips, and R.M. Haralick, “Table Structure Understanding and Its Performance Evaluation,” Pattern Recognition, vol. 37, no. 7, pp. 1479-1497, 2004.
[13] L.B. Kara and T.F. Stahovich, “Hierarchical Parsing and Recognition of Hand-Sketched Diagrams,” Proc. 17th ACM Symp. User Interface Software and Technology, pp. 13-22, 2004.
[14] C. Alvarado, “A Framework for Multi-Domain Sketch Recognition,” Proc. AAAI Spring Symp. Sketch Understanding, AAAI Technical Report SS-02-08, Stanford Univ., pp. 1-8, 2002.
[15] S.L. Taylor, R. Fritzson, and J.A. Pastor, “Extraction of Data from Preprinted Forms,” Machine Vision and Applications, vol. 5, pp. 211-222, 1992.
[16] J.J. LaViolaJr. and R.C. Zeleznik, “MathPad$^2$ : A System for the Creation and Exploration of Mathematical Sketches,” ACM Trans. Computer Graphics, vol. 24, no. 3, pp. 432-440, 2004.
[17] J. Liang, “Document Structure Analysis and Performance Evaluation,” PhD thesis, Univ. of Washington, Seattle, 1999.
[18] M. Hurst, “Layout and Language: An Efficient Algorithm for Detecting Text Blocks Based on Spatial and Linguistic Evidence,” Proc. Document Recognition and Retrieval VIII (IS & T/SPIE Electronic Imaging), vol. 4307, pp. 56-67, 2001.
[19] E. Saund, “Finding Perceptually Closed Paths in Sketches and Drawings,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 4, pp. 475-491, Apr. 2003.

Index Terms:
Table detection, table recognition, graphics recognition, handwriting recognition, document analysis, pen-based computing.
Citation:
Zhouchen Lin, Junfeng He, Zhicheng Zhong, Rongrong Wang, Heung-Yeung Shum, "Table Detection in Online Ink Notes," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 8, pp. 1341-1346, Aug. 2006, doi:10.1109/TPAMI.2006.173
Usage of this product signifies your acceptance of the Terms of Use.