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Fast and reliable acquisition of truth data for document analysis using cyclic suggest algorithms

  • In document analysis the availability of ground truth data plays a crucial role for the success of a project. This is even more true at the rise of new deep learning methods which heavily rely on the availability of training data. But even for traditional, hand crafted algorithms that are not trained on data, reliable test data is important for the improvement and evaluation of the methods. Because ground truth acquisition is expensive and time consuming, semi-automatic methods are introduced which make use of suggestions coming from document analysis systems. The interaction between the human operator and the automatic analysis algorithms is the key to speed up the process while improving the quality of the data. The final confirmation of data may always be done by the human operator. This paper demonstrates a use case for acquisition of truth data in a mail processing system. It shows why a new, extended view on truth data is necessary in development and engineering of such systems. An overview over the tool and the data handling is given, the advantages in the workflow are shown, and consequences for the construction of analysis algorithms are discussed. It can be shown that the interplay between suggest algorithms and human operator leads to very fast truth data capturing. The surprising finding is the fact that if multiple suggest algorithms circularly depend on data, they are especially effective in terms of speed and accuracy.

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Parent Title (English):2nd International Workshop on Open Services and Tools for Document Analysis (ICDAR-OST), with International Conference on Document Analysis and Recognition Workshops (ICDAR 2019), 22-25 Sept. 2019, Sydney, Australia
Editor:Marc-Peter Schambach, Stephan von der Nüll, Martin Schall
Document Type:Conference Proceeding
Year of Publication:2019
Release Date:2020/01/21
First Page:7
Last Page:12
Volltextzugriff für Angehörige der Hochschule Konstanz möglich
Institutes:Institut für Optische Systeme - IOS
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)
Open Access?:Nein
Licence (German):License LogoUrheberrechtlich geschützt