Extracting the Hidden: Paper Watermark Location and Identification

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Extracting the Hidden Paper Watermark Location
and Identification
School of Computing FACULTY OF ENGINEERING

• Roger Boyle, roger_at_comp.leeds.ac.uk
• Kia Ng, kia_at_comp.leeds.ac.uk

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Gants (2000)

• the crucial understanding of paper, the
  commodity that links all the individual
  warehouses bookstalls, and printing houses. Gants
  D

The study of watermarks is a seductive if
somewhat esoteric pastime. While it is normally
the beauty and aesthetic quality of watermarks
that initially attract the researcher, they are
more than just pretty affectations and can shed
light on historic trends and events. Pavelka
.
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Motivation
The manufacture, trading, retailing and use of
paper are key aspects in attribution. But there
are also core codicological interests in
determining what was made and used by whom and
when. The watermark is the best known signature
of a paper mould. There are others arguably
better.
.
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Paper

• Watermarks
• Chain lines
• Laid lines
• Paper texture
• Twins

Watermarks in Incunabula printed in the Low
Countries http//watermark.kb.nl/
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Motivation

• Increasing availability of digital repositories
• Improvements in underlying pattern recognition
  and extraction techniques
• The right place at the right time

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Challenges

• Hidden (by design)
• Many documents of interest are
• Delicate
• In private collections
• Inscribed recto and verso
• Obstructions and interference
• Defects, e.g. Fold marks, paper texture, etc

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Back-lighting Acquisition System
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Example Input
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Back-light
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Back-light Contrast
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Overall Framework
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Layers

• Separate the input image into several layers
• Ia removing foreground interference, e.g.
  writing
• Ib Non-uniform background , e.g. texture,
  noise, folding, etc
• Iw Ia - Ib watermark (and some residual
  noise)
• Use morphological operations to suppress
  interference
• A combination of morphological dilation (C A ?
  B) and erosion (C A ? B) operations
• where A the image and B the structuring element

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Element Size B

• Applying a contrast stretching process
• the darkest pixels zero intensity value
• Find the percentage of such pixels x
• Within the original image, determine the grey
  level g such that x of pixels are lt intensity
  g
• Dilate the input image, starting with structuring
  element of size 1, and increasing the size
• until all pixels values gt g
• ? optimal structuring size to remove foreground
  interference.

Number of pixels of values below g plotted
against structuring element size
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Background Estimation

• To estimate the image background
• Remove the watermark pattern
• Find a structuring element size that is large
  enough to cover a single feature of the pattern
• Opening is useful for separating touching
  features, and removing small regions and sharp
  peaks.
• Morphological top-hat transform,
• A - (A ? B), where ? is morphological opening
• C A ? B (A ? B) ? B
• A the image and B the structuring element

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Watermark Size

• Now possible, after the removal of obstructing
  foreground features
• Applying a series of morphological openings with
  structuring elements of increasing size
• The sum of pixel intensity values in the output
  image after each opening is stored

Cumulative intensities plotted against
structuring element radius
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Difference

• Difference of total intensities between two
  sequential openings
• distribution of objects sizes at that scale
• the pattern spectrum of the image
• a local minimum at a specific radius will
  indicate the existence of many image objects of
  that radius.
• The global minimum, Rmin, indicate the highest
  cumulative intensity of objects at that radius.

Granulometry (size distribution ) of image objects
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Difference Contrast
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Top-down approach

• Our approach has been demonstrated successfully
  on a range of inputs.
• But it will fail on challenging data exhibiting
  thick paper, heavy interference, or damage.

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Hard data

• The Mahdiyya Quran

• Part of the elaborate Leeds Arabic collection
• Pillage from the battle of Omdurman
• Studied in detail by Brockett (1987)

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What you see
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What you see
A watermark is just discernible in the RH
margin. Most details are very faint. Fainter
examples exist in the text.
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What we do

• Rather than seek results bottom up,
  pixel-by-pixel, we construct a computational
  model of what the backlighting does.

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What we do
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What we do
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What we do
Leading to a representation of just the verso and
the interior.
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What we do
And thus we derive a picture that isolates the
information not visible as either recto or
verso. This includes the watermark and mould
features, but also paper irregularities and
various other noise.
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What we see

• Statistical attacks and top-down reasoning can
  betray the presence of incomplete, damaged
  patterns.
• Capturing watermark fragments incomplete and
  possibly inaccurate is straightforward from
  some pages

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What we see
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What we see
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Potential

• Reliable identification of these partial patterns
  permits by aggregation recapturing of patterns
  not seen before

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Undiscovered countermark information
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Potential

• We can study subtleties of manufacture

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Conclusions

• That which lies within is often as valuable as
  the inscription
• Sometimes, wrestling with the paper is essential
• These are not new problems enhanced digital
  access is new
• Computer science can bring tools from other
  domains of significant benefit
• ( and benefit itself)

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References
Hazem Hiary and Kia Ng, A System for Segmenting
and Extracting Paper-based Watermark Designs,
International Journal on Digital Libraries,
6(4)351-361, Springer, 2007. Roger Boyle and
Hazem Hiary, Watermark location via back-lighting
and recto removal, International Journal of
Document Analysis and Recognition, 12(1), 33-,
2009
Thank you
Thanks to the Special Collections of the Leeds
University Library for the manuscripts and other
test samples used in this research project, and
ICSRiM (www.icsrim.org.uk) for the acquisition
system.