Learning to Recognize Critical Cells in Document Tables

Tables are among the most informative components of documents, because they are exploited to compactly and intuitively represent data, typically for understandability purposes. The needs are to identify and extract tables from documents, and, on the other hand, to be able to extract the data they contain. The latter task involves the understanding of a table structure. Due to the variability in style, size, and aims of tables, algorithmic approaches to this task can be insufficient, and the exploitation of machine learning systems may represent an effective solution. This paper proposes the exploitation of a first-order logic representation, that is able to capture the complex spatial relationships involved in a table structure, and of a learning system that can mix the power of this representation with the flexibility of statistical approaches. The obtained encouraging results suggest further investigation and refinement of the proposal.