Bull's Head and Mermaid - The Bernstein Project - Österreichische ...

Ill. 10: Coat of arms
All these properties are exploited in order to extract the watermark.
In practice this means that all kind of mathematical
operations are performed and that thresholds are applied
to detect those points which belong to the watermark
and which ones not. For finding the appropriate thresholds
a training set is used. It consists of a representative set of
watermarks, reflecting all kind of difficulties, like poor contrast
of the watermark lines, white spots produced by
metallic particles in the paper, large variation in background
intensity values et cetera. With the help of this training set
the optimal thresholds are determined.
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For imagery with relatively little noise the results are quite
well (Ill. 11). The results are even better than until now
known in literature.
However, in case of very noisy imagery (Ill. 12) the results
are more problematic, although it depends on the application.
For matching (finding identical watermarks) most of
the time this result is sufficient. But as a matter of fact not
in the case that one wants to use the watermark for the
generation of watermark catalogues. It may not be expected
that the method will work for all watermarks in all cases.
That means that it can be completely automatic. And
this moment research is performed how to combine automatic
methods with semi-automatic methods for the difficult
watermarks in very noisy imagery.
Watermark extraction in the Piccard-Online
database and the printed Piccard
As mentioned above watermark detection is rather easy in
case of binary imagery, e.g. linedrawings. Here it is sufficient
to use a rather simple method like thresholding. That means
all pixels above that threshold are assumed to belong to the
watermark and all pixels below that value belong to the background.
In this case it is easy to generate a digital image of the
watermark. However, in practice this is less simple. Consider
for instance the Piccard-Online database at the Hauptstaatsarchiv
Stuttgart (see also Chapter VI). It is the largest watermark
collection in the world. It contains 92,000 different
records. They are classified according to 38 categories, representing
e.g. crown, bull’s head, tower, cross, horn. Every single
watermark within the Piccard-Online database is stored in a
separate digital image file. But this digital representation does
not mean that for computer applications it is a priori known
which point belongs to the watermark and which one not.
Here, simple thresholding is not sufficient. In addition to the
watermark itself there is also extra information. Surrounding
the watermark there are different elements (Ill. 4) as chain
lines, laid lines and some text and numbers which give extra information
about the watermark and about where the paper
comes from. For a computer it is in the beginning not clear to
distinguish between the watermark and the other informa-
Ill. 11: Detection of two watermarks from the watermark database of the Royal Library of the Netherlands