title: Table detection in online ink notes.
abstract: 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 primative 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.
authors.affiliation: Microsoft Research Asia, 5th Floor, Sigma Building Zhichun Road #49, Haidian District, Beijing 100080, PR China. zhoulin@microsoft.com
authors.forename: Zhouchen
authors.initials: Z
authors.lastname: Lin
authors.forename: Junfeng
authors.initials: J
authors.lastname: He
authors.forename: Zhicheng
authors.initials: Z
authors.lastname: Zhong
authors.forename: Rongrong
authors.initials: R
authors.lastname: Wang
authors.forename: Heung-Yeung
authors.initials: HY
authors.lastname: Shum
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003196:Computer Graphics; D004282:Documentation / Q000379:methods*; D001330:Electronic Data Processing / Q000379:methods*; D006236:Handwriting; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods*; D013223:Statistics as Topic; D014584:User-Computer Interface