Publication
ICDAR 2017
Conference paper

A Rectangle Mining Method for Understanding the Semantics of Financial Tables

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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.

Date

02 Jul 2017

Publication

ICDAR 2017

Authors

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