![](bg6.png)
However it provided us with a natural way of generat-
ing fuzzy ground truth in the context of the ”anyOCR”
pipeline proposed in (Jenckel et al., 2016).
The scope of this paper only covered the feasibility
of non-expert annotations which was shown through
the use of synthetic data. Therefore we plan further
evaluation with real non-expert annotations. While
we have shown that training on fuzzy ground truth can
be beneficial in the area of historical documents, fur-
ther analysis on different scripts and document types
is needed as well.
In the future we plan to further explore the possibili-
ties of using fuzzy ground truth when training LSTM
networks, like ground truth options with different seg-
mentations like ”m” and ”in”. Another future fo-
cus will be on automatically generating fuzzy ground
truth. While the proposed method reduces the need
for language experts it is still costly to annotate the
data by hand.
ACKNOWLEDGEMENTS
This work was partially funded by the BMBF (Ger-
man Federal Ministry of Education and Research),
project Kallimachos (01UG1415C).
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