TY - CHAP

T1 - Extraction of information from invoices - challenges in the extraction pipeline

AU - Thiée, Lukas Walter

AU - Krieger, Felix

AU - Funk, Burkhardt

N1 - Conference code: 53

PY - 2023

Y1 - 2023

N2 - Data from invoices are key information for business processes. In order to use the data and create business value, the information must be captured in a digital and structured form. Leveraging digital tools and AI/ML is state-of-The-Art in the extraction of information from invoices. However, the existing approaches are trained on specific languages and layouts, and while focusing on the performance of individual metrics, they neglect the demonstration of the pipeline from raw data to processable information. In this paper, we investigate the types of information on invoices and address the challenges in the extraction pipeline. We contribute by providing a morphological framework for the problematization and design of a pipeline as part of a design science study.

AB - Data from invoices are key information for business processes. In order to use the data and create business value, the information must be captured in a digital and structured form. Leveraging digital tools and AI/ML is state-of-The-Art in the extraction of information from invoices. However, the existing approaches are trained on specific languages and layouts, and while focusing on the performance of individual metrics, they neglect the demonstration of the pipeline from raw data to processable information. In this paper, we investigate the types of information on invoices and address the challenges in the extraction pipeline. We contribute by providing a morphological framework for the problematization and design of a pipeline as part of a design science study.

KW - Data pipeline.

KW - Information extraction

KW - Invoice recognition

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=85181143950&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/aeef32be-854c-3f08-9b99-86cf2576e3ed/

U2 - 10.18420/inf2023_180

DO - 10.18420/inf2023_180

M3 - Article in conference proceedings

AN - SCOPUS:85181143950

T3 - GI-Edition: Lecture Notes in Informatics (LNI), Proceedings

SP - 1777

EP - 1792

BT - INFORMATIK 2023

A2 - Klein, Maike

A2 - Krupka, Daniel

A2 - Winter, Cornelia

A2 - Wohlgemuth, Volker

PB - Gesellschaft für Informatik e.V.

CY - Bonn

T2 - 53. Annual Meeting of the German Informatics Society (GI) - INFORMATICS 2023

Y2 - 26 September 2023 through 29 September 2023

ER -