Data Driven Humanities. May 18th 2018. Data Driven innovation 2018. Engineering Department, University of Roma Tre

1. In Codice RatioIn Codice Ratio
Mining the Vatican Secret ArchivesMining the Vatican Secret Archives

6. Lector
an ongoing research project at RM3
in collaboration with UofA
3M new facts
extracted from Wikipedia

7. Rome hosts
the largest historical archive
in the world
Rome hosts
the largest historical archive
in the world

8. How large is the vatican secret archive?
All shelvings
of the
Archive,
vertically
stacked
85 km
Mt. Everest
8.8 km

9. What’s in the Archive?

12. Let’s take one step back: transcription
-eptus. Cum igitur unicu(m) obtinere te asseras beneficiu(m) curans h(abe)ns a(n)i(m)ar(um) anne-
xam de cuius p(ro)ventib(us) congrue sustentari no(n) potes nos tuis precib(us) i(n)clinati
presentiu(m) t(ib)i auctoritate concedim(us) ut unu(m) aliud beneficiu(m) cui cura similiter
sit a(n)i(m)ar(um) annexa si t(ib)i canonice offeratur possis de p(er)missio(n)e n(ost)ra recip(er)e et cum
illo primum lib(er)e retinere. Nulli (er)go et c(etera) n(ost)re concessionis infringere. Siq(ui)s aut(em)
Input:
image
Output:
text

13. ‘nobis’

14. Problem: scalability
# of occurrences
# of words
5 words have 1000
occurrences
1 word has 10000
occurences
100 words have
less than 5
occurrences
We would need
to transcribe
thousands of
pages
Years of expert
work

15. ‘g’

16. Crowdsourcing
(2016)
120 high school students
25 symbols
263029 images
Every image labeled by 4 students
1M labelled images
13008 positive examples

17. Deep Convolutional Neural Networks

18. Deep Convolutional Neural Networks

19. Our Model
input
Conv
Layer
42 5x5
Units
+
ReLU
activ
2x2 stride 2
Maxpooling Conv
Layer
42 5x5
Units
+
ReLU
activ
2x2 stride 2
Maxpooling
Conv
Layer
28 5x5
Units
+
ReLU
activ
2x2 stride 2
Maxpooling
flatten
FC
Layer
200
Units
+
ReLU
activ
Softmax
Output
Layer
23
classes56x56
binary
image
0.5 dropout
0.5 dropout
0.5 dropout
0.5 dropout

21. Problem solved?
P(anno) = 1,7*10‐7
P(aiiiio) = 2,4*10‐21

22. Results
http://www.inf.uniroma3.it/db/icr/preliminary‐results.html
https://arxiv.org/pdf/1803.03200.pdf

23. Crowdsourcing (2017)

24. Experiment with sequence prediction models
…and/or by aligning existing transcription
“hactenus degui pulsare manu sancti desiderii
ut mirabilium patratoris edmundi regis”

25. Team
PAOLO MERIALDO
Professor
Università degli Studi
Roma Tre
Dipartimento di
Ingegneria
paolo.merialdo
@uniroma3.it
DONATELLA FIRMANI
Post‐doc
Università degli Studi
Roma Tre
Dipartimento di Ingegneria
donatella.firmani
@uniroma3.it
SIMONE SCARDAPANE
Post‐doc
Università degli Studi
La Sapienza
Dipartimento di Ingegneria
dell'Informazione, Elettronica e
Telecomunicazioni
simone.scardapane
@uniroma1.it
ELENA NIEDDU
PhD student
Università degli Studi
Roma Tre
Dipartimento di Ingegneria
elena.nieddu
@uniroma3.it
SERENA AMMIRATI
Professor
Università degli Studi
Roma Tre
Dipartimento Studi
Umanistici
serena.ammirati
@uniroma3.it
MARCO MAIORINO
Professor
Archivio Segreto Vaticano
Scuola Vaticana di Paleografia,
Diplomatica e Archivistica
marco.maiorino2109
@gmail.com

26. Thank you!