A Rectangle Mining Method for Understanding the Semantics of Financial Tables

Abstract

Financial statements report crucial information in tables with complex semantic structure, which are desirable, yet challenging, to interpret automatically. For example, in such tables a row of data cells is often explained by the headers of other rows. In a departure from prior art, we propose a rectangle mining framework for understanding complex tables, which considers rectangular regions rather than individual cells or pairs of cells in a table. We instantiate this framework with ReMine, an algorithm for extracting row header semantics of table, and show that it significantly outperforms prior pair-wise classification approaches on two datasets: (i) a set of manually labeled financial tables from multiple companies, and (ii) the ICDAR 2013 Table Competition dataset.