1. Filigranes pour tous Watermarks For All
A new project based on deep-learning technology and crowd-sourcing
Marc H. Smith
École nationale des chartes / Centre-Jean Mabillon
Paris Sciences & Lettres
Watermarks in digital collections
4th International Conference
Vienna, October 2017

5. « Science des données, données de la science »
IRIS – Initiative de recherche interdisciplinaire et stratégique
École nationale des chartes
Christine Bénévent, Olivier Poncet, Marc Smith
École des Ponts ParisTech
Mathieu Aubry
INRIA – Institut national de recherche en informatique et en automatique
Joseph Sivic
IRHT – Institut de recherche et d’histoire des textes
François Bougard, Bruno Bon

6. Repertories of watermarks: evolution and limitations
From drawings to photographs
From paper to digital
From single/national corpora to portals and interoperability
Limitations:
– Identifying watermarks: image > word > image
– Number of reference images: more often “similar” than identical
– Closed data, from producer to user

7. Filigranes pour tous
Identification : image to image
Deep-learning technology for image comparison
Initial corpus: French watermarks > international collaboration?
User interaction: image matching and database augmentation
> Multiple images of (identical or variant) watermarks

8. Test corpus
Set of homogeneous watermarks from French archives
Notarial records from the Archives nationales (1650)
4 different watermarks × 61 photographs
using 3 lightsheets and 3 smartphones
Minimal guidelines for framing.
Pages with and without writing

9. Test sample: four watermarks

10. Random sample of multiple occurrences of a watermark

11. Image capture and pre-processing

12. Image capture and pre-processing
1/6
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13. Image capture and pre-processing
1/6
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300 x 300 pixels
1/6
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14. Deep learning Convolutional neural network:
Iteration of simple operations with multiple parameters
Parameters are optimized on training data, producing a different result for each watermark …
Image
Layer 1
Layer 2
classifier

15. Elementary operation of a single ‘neuron’
x = input, w = parameters

16. Image matching: first results
Training set: 200 images (50 / watermark )
100% correct matching
Control set: 44 images (11 / watermark)
95 % correct matching (42/44)
Caution: “black box” syndrome:
is the matching actually based on watermarks?

17. Further development: the app
Tools for image capture:
Ruler & framing mask > scale
Real-time uploading and image comparison
User-uploaded images and metadata added to the database

18. Open questions
Expanding the data set: how will the software adapt?
Minimum training data set? (a single image?)
Fragmentary/partially visible watermarks (sub-folio quires)
Capture: close-ups vs full pages — at 300 × 300 pix !
Comparing photographs and drawings?
Stimulation of crowdsourcing

19. Research questions
Quantitative measurements
Watermark variants and evolution: copies, deterioration, etc.
Paper history: from production to circulation and consumption
Functional distribution of formats and quality:
books vs documents vs art…