TY  - CHAP
U1  - Konferenzveröffentlichung
ED  - Schambach, Marc-Peter
ED  - von der Nüll, Stephan
ED  - Schall, Martin
T1  - Fast and reliable acquisition of truth data for document analysis using cyclic suggest algorithms
T2  - 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
N2  - 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.
Y1  - 2019
SN  - 978-1-7281-5055-0
SB  - 978-1-7281-5055-0
SN  - 978-1-7281-5054-3
SB  - 978-1-7281-5054-3
U6  - https://doi.org/10.1109/ICDARW.2019.10030
DO  - https://doi.org/10.1109/ICDARW.2019.10030
N1  - Volltextzugriff für Angehörige der Hochschule Konstanz möglich
SP  - 7
EP  - 12
PB  - IEEE
ER  -