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

In: Innovation Through Information Systems

Author

Listed:
  • Felix Krieger

    (Leuphana Universität)

  • Paul Drews

    (Leuphana Universität)

  • Burkhardt Funk

    (Leuphana Universität)

  • Till Wobbe

    (EY, GSA Assurance Research and Development)

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.

Suggested Citation

  • Felix Krieger & Paul Drews & Burkhardt Funk & Till Wobbe, 2021. "Information Extraction from Invoices: A Graph Neural Network Approach for Datasets with High Layout Variety," Lecture Notes in Information Systems and Organization, in: Frederik Ahlemann & Reinhard Schütte & Stefan Stieglitz (ed.), Innovation Through Information Systems, pages 5-20, Springer.
    • Handle: RePEc:spr:lnichp:978-3-030-86797-3_1
    DOI: 10.1007/978-3-030-86797-3_1
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