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The Venice Time Machine 
A new way of navigating in the past 
Frédéric Kaplan Dorit Raines 
Ecole Polytecnique Fédérale de Lausanne Università Ca’ Foscari Venice
Battista Nani (1616-1678), Venetian ambassador, writer, historiographer
Birth act of Battista 
Nani in the Venetian 
State Archives
B9 raines venice_timemachine_minimized
B9 raines venice_timemachine_minimized
B9 raines venice_timemachine_minimized
B9 raines venice_timemachine_minimized
B9 raines venice_timemachine_minimized
The Frari convent – today’s State Archives of Venice
The ambition of The Venice Time Machine project is to transform 
this huge archive into a digital information system.
How can one find all records 
regarding Battista Nani in 
80 km. of documents?
The Venice Time Machine project 
aims to address this question by 
structuring the information 
contained in the records
1. Digitization of all documents 
2. Semi-automatic transcription 
of handwritten documents 
3. Data mining and structured 
data base 
* We are currently in the experimental phase
Digitization of documents
Virtual X-ray reading 
“Reading” manuscripts using x-ray tomography. 
A single tomographic set could indeed yield the same information 
of traditional digitization process with minimized interaction with 
the document, drastically reducing the risk of damage and speeding 
up the process.
The X-ray imaging of the writings is made possible by the use, for 
many centuries and all over Europe, of iron-based inks generically 
denominated “iron gall”. 
There is a direct correlation between the ink iron content and the 
quality of x-ray attenuation-contrast images: a fundamental issue of 
the studies is the chemistry of the inks.
B9 raines venice_timemachine_minimized
Semi-automatic Transcription of 
Handwritten Documents 
The framework based on machine-vision and machine-learning 
techniques is comprised of the following steps:
1. Pre-processing algorithms used to enhance the readability of the 
textual content 
Optimal binarization
2. The pre-filtered images go through a classification process with the purpose of classifying 
the document contents in graphical and textual elements and layout structure
3. The textual content is finally processed using a semi-automatic transcription tool in order 
incrementally construct a digital version of the documents
B9 raines venice_timemachine_minimized
Data mining and structured data base 
Each archival series will need its own structure (reduced to minimum to match others) 
BUT 
On a metacontent level ONLY 4 categories are important: 
 Persons 
 Places 
 Institutions 
 Objects 
+ date 
The search will be done using the above 4 categories (metacontent), with sub divisions: 
METACONTENT: Painting 16th century; GRANULAR LEVEL: Titian; woman 
METACONTENT: Pietro Basadonna 1675; GRANULAR LEVEL: Capuchin order; 
mansionary 
Each source creates the link between dates and facts
Let’s turn back to Battista Nani
B9 raines venice_timemachine_minimized
B9 raines venice_timemachine_minimized
B9 raines venice_timemachine_minimized
B9 raines venice_timemachine_minimized
B9 raines venice_timemachine_minimized
B9 raines venice_timemachine_minimized
B9 raines venice_timemachine_minimized
But we can do better still – we can create 3D representations of Venice and 
link documents to each palace in order to tell its history and the urbanistic 
changes throughout the ages
The Rialto bridge area - 1500
A 3D rendering of the Rialto bridge area
B9 raines venice_timemachine_minimized
B9 raines venice_timemachine_minimized
And many more projects to come! 
Garzoni. Aprenticeship, 
labor and society, by 
Valentina Sapienza
The Venice Time Machine Project 
Our website: dhvenice.eu 
Partner Institutions 
Archivio di Stato di Venezia, direttore Raffaele Santoro 
École Polytechnique Fédérale de Lausanne (EPFL), president Patrick Aebischer 
Università Ca’ Foscari di Venezia, rettore Michele Bugliesi 
With the support of 
Fondation Lombard Odier 
Director 
Frédéric Kaplan, École Polytechnique Fédérale de Lausanne (EPFL) 
Project Management 
Giovanni Colavizza, École Polytechnique Fédérale de Lausanne (EPFL) 
Isabella di Lenardo, École Polytechnique Fédérale de Lausanne (EPFL) 
Katerina Kunz, École Polytechnique Fédérale de Lausanne (EPFL) 
Andrea Mazzei, École Polytechnique Fédérale de Lausanne (EPFL) 
Giovanni Caniato, Archivio di Stato di Venezia 
Simon Levis Sullam, Università Ca’ Foscari di Venezia 
Dorit Raines, Università Ca’ Foscari di Venezia 
Partner projects 
“Garzoni project”, coordinator Valentina Sapienza, Université de Lille 
“Visualizing Venice”, Steering Committee: Caroline Bruzelius, Donatella Calabi, Andrea Giordano, Mark Olson, Andrea 
Rinaldo, Victoria Szabo, Guido Zucconi. 
Technical support 
4DigitalBooks, Ivo Iossiger, scanner provider, Ecublens, Switzerland 
Bread and Butter, digital media, Lausanne, Switzerland 
AMstudio, 3D modelling, Venezia, Italy 
Olivo Bondesan, foto-riproduzione, Archivio di Stato di Venezia 
Thank you!

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B9 raines venice_timemachine_minimized

  • 1. The Venice Time Machine A new way of navigating in the past Frédéric Kaplan Dorit Raines Ecole Polytecnique Fédérale de Lausanne Università Ca’ Foscari Venice
  • 2. Battista Nani (1616-1678), Venetian ambassador, writer, historiographer
  • 3. Birth act of Battista Nani in the Venetian State Archives
  • 9. The Frari convent – today’s State Archives of Venice
  • 10. The ambition of The Venice Time Machine project is to transform this huge archive into a digital information system.
  • 11. How can one find all records regarding Battista Nani in 80 km. of documents?
  • 12. The Venice Time Machine project aims to address this question by structuring the information contained in the records
  • 13. 1. Digitization of all documents 2. Semi-automatic transcription of handwritten documents 3. Data mining and structured data base * We are currently in the experimental phase
  • 15. Virtual X-ray reading “Reading” manuscripts using x-ray tomography. A single tomographic set could indeed yield the same information of traditional digitization process with minimized interaction with the document, drastically reducing the risk of damage and speeding up the process.
  • 16. The X-ray imaging of the writings is made possible by the use, for many centuries and all over Europe, of iron-based inks generically denominated “iron gall”. There is a direct correlation between the ink iron content and the quality of x-ray attenuation-contrast images: a fundamental issue of the studies is the chemistry of the inks.
  • 18. Semi-automatic Transcription of Handwritten Documents The framework based on machine-vision and machine-learning techniques is comprised of the following steps:
  • 19. 1. Pre-processing algorithms used to enhance the readability of the textual content Optimal binarization
  • 20. 2. The pre-filtered images go through a classification process with the purpose of classifying the document contents in graphical and textual elements and layout structure
  • 21. 3. The textual content is finally processed using a semi-automatic transcription tool in order incrementally construct a digital version of the documents
  • 23. Data mining and structured data base Each archival series will need its own structure (reduced to minimum to match others) BUT On a metacontent level ONLY 4 categories are important: Persons Places Institutions Objects + date The search will be done using the above 4 categories (metacontent), with sub divisions: METACONTENT: Painting 16th century; GRANULAR LEVEL: Titian; woman METACONTENT: Pietro Basadonna 1675; GRANULAR LEVEL: Capuchin order; mansionary Each source creates the link between dates and facts
  • 24. Let’s turn back to Battista Nani
  • 32. But we can do better still – we can create 3D representations of Venice and link documents to each palace in order to tell its history and the urbanistic changes throughout the ages
  • 33. The Rialto bridge area - 1500
  • 34. A 3D rendering of the Rialto bridge area
  • 37. And many more projects to come! Garzoni. Aprenticeship, labor and society, by Valentina Sapienza
  • 38. The Venice Time Machine Project Our website: dhvenice.eu Partner Institutions Archivio di Stato di Venezia, direttore Raffaele Santoro École Polytechnique Fédérale de Lausanne (EPFL), president Patrick Aebischer Università Ca’ Foscari di Venezia, rettore Michele Bugliesi With the support of Fondation Lombard Odier Director Frédéric Kaplan, École Polytechnique Fédérale de Lausanne (EPFL) Project Management Giovanni Colavizza, École Polytechnique Fédérale de Lausanne (EPFL) Isabella di Lenardo, École Polytechnique Fédérale de Lausanne (EPFL) Katerina Kunz, École Polytechnique Fédérale de Lausanne (EPFL) Andrea Mazzei, École Polytechnique Fédérale de Lausanne (EPFL) Giovanni Caniato, Archivio di Stato di Venezia Simon Levis Sullam, Università Ca’ Foscari di Venezia Dorit Raines, Università Ca’ Foscari di Venezia Partner projects “Garzoni project”, coordinator Valentina Sapienza, Université de Lille “Visualizing Venice”, Steering Committee: Caroline Bruzelius, Donatella Calabi, Andrea Giordano, Mark Olson, Andrea Rinaldo, Victoria Szabo, Guido Zucconi. Technical support 4DigitalBooks, Ivo Iossiger, scanner provider, Ecublens, Switzerland Bread and Butter, digital media, Lausanne, Switzerland AMstudio, 3D modelling, Venezia, Italy Olivo Bondesan, foto-riproduzione, Archivio di Stato di Venezia Thank you!