Abstract
Table characteristics vary widely. Consequently, a great variety of computational approaches have been applied to table recognition. In this survey, the table recognition literature is presented as an interaction of table models, observations, transformations, and inferences. A table model defines the physical and logical structure of tables; the model is used to detect tables and to analyze and decompose the detected tables. Observations perform feature measurements and data lookup, transformations alter or restructure data, and inferences generate and test hypotheses. This presentation clarifies both the decisions made by a table recognizer and the assumptions and inferencing techniques that underlie these decisions.
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