Few-exemplar Information Extraction for Business Documents
Daniel E. Esser, Daniel Schuster, Klemens Muthmann, Alexander Schill · 2014
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 effi- ciency 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 efficiency. Our evaluation on a document set of 12,500 documents following 399 different layouts/templates shows 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.