Information Extraction from Invoices: A Graph Neural Network Approach for Datasets with High Layout Variety

Felix Krieger (), Paul Drews (), Burkhardt Funk () and Till Wobbe ()
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Felix Krieger: Leuphana Universität
Paul Drews: Leuphana Universität
Burkhardt Funk: Leuphana Universität
Till Wobbe: EY, GSA Assurance Research and Development

A chapter in Innovation Through Information Systems, 2021, pp 5-20 from Springer

Abstract: Abstract Extracting information from invoices is a highly structured, recurrent task in auditing. Automating this task would yield efficiency improvements, while simultaneously improving audit quality. The challenge for this endeavor is to account for the text layout on invoices and the high variety of layouts across different issuers. Recent research has proposed graphs to structurally represent the layout on invoices and to apply graph convolutional networks to extract the information pieces of interest. However, the effectiveness of graph-based approaches has so far been shown only on datasets with a low variety of invoice layouts. In this paper, we introduce a graph-based approach to information extraction from invoices and apply it to a dataset of invoices from multiple vendors. We show that our proposed model extracts the specified key items from a highly diverse set of invoices with a macro $${F}_{1}$$ F 1 score of 0.8753.

Keywords: Graph attention networks; Unstructured data; Audit digitization; Graph-based machine learning (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/978-3-030-86797-3_1

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