Few-exemplar Information Extraction for Business Documents

Daniel Esser, Daniel Schuster, Klemens Muthmann, Alexander Schill


The automatic extraction of relevant information from business documents (sender, recipient, date, etc.) is a valuable task in the application domain of document management and archiving. Although current scientific and commercial self-learning solutions for document classification and extraction work pretty well, they still require a high effort of on-site configuration done by domain experts and administrators. Small office/home office (SOHO) users and private individuals do often not benefit from such systems. A low extraction effectivity especially in the starting period due to a small number of initially available example documents and a high effort to annotate new documents, drastically lowers their acceptance to use a self-learning information extraction system. Therefore we present a solution for information extraction that fits the requirements of these users. It adopts the idea of one-shot learning from computer vision to the domain of business document processing and requires only a minimal number of training to reach competitive extraction effectivity. Our evaluation on a document set of 12,500 documents consisting of 399 different layouts/templates achieves extraction results of 88% F1 score on 10 commonly used fields like document type, sender, recipient, and date. We already reach an F1 score of 78% with only one document of each template in the training set.


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Paper Citation

in Harvard Style

Esser D., Schuster D., Muthmann K. and Schill A. (2014). Few-exemplar Information Extraction for Business Documents . In Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-027-7, pages 293-298. DOI: 10.5220/0004946702930298

in Bibtex Style

author={Daniel Esser and Daniel Schuster and Klemens Muthmann and Alexander Schill},
title={Few-exemplar Information Extraction for Business Documents},
booktitle={Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},

in EndNote Style

JO - Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Few-exemplar Information Extraction for Business Documents
SN - 978-989-758-027-7
AU - Esser D.
AU - Schuster D.
AU - Muthmann K.
AU - Schill A.
PY - 2014
SP - 293
EP - 298
DO - 10.5220/0004946702930298