A Position-Based Method for the Extraction of Financial Information in PDF Documents

Financial documents are omnipresent and necessitate extensive human efforts in order to extract, validate and export their content. Considering the high importance of such data for effective business decisions, the need for accuracy goes beyond any attempt to accelerate the process or save resources. While many methods have been suggested in the literature, the problem to automatically extract reliable financial data remains difficult to solve in practice and even more challenging to implement in a real life context. This difficulty is driven by the specific nature of financial text where relevant information is principally contained in tables of varying formats. Table Extraction (TE) is considered as an essential but difficult step for restructuring data in a handleable format by identifying and decomposing table components. In this paper, we present a novel method for extracting financial information by the means of two simple heuristics. Our approach is based on the idea that the position of information, in unstructured but visually rich documents - as it is the case for the Portable Document Format (PDF) - is an indicator of semantic relatedness. This solution has been developed in partnership with the Caisse de Depot et Placement du Québec. We present here our method and its evaluation on a corpus of 600 financial documents, where an F-measure of 91% is reached.