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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.
Table detection, table recognition, graphics recognition, handwriting recognition, document analysis, pen-based computing.
Zhouchen Lin, Heung-Yeung Shum, Rongrong Wang, Zhicheng Zhong, Junfeng He, "Table Detection in Online Ink Notes", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 1341-1346, August 2006, doi:10.1109/TPAMI.2006.173
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