Rome Wasn't Digitized in a Day - Council on Library and Information ...

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Although the application named DUGA that has been created by the eSAD project is based on decision
support system technology (DSS) such as that used by doctors and eng<strong>in</strong>eers, they ultimately decided it
was an <strong>in</strong>terpretation support system they were creat<strong>in</strong>g s<strong>in</strong>ce “experts transcrib<strong>in</strong>g ancient documents
do not make decisions based on evidence but <strong>in</strong>stead create <strong>in</strong>terpretations of the texts based on their
perception” (Olsen et al. 2009). At the same time, one of the key research goals was to explore issues
of technology transfer, or to see if the ideas <strong>in</strong>volved <strong>in</strong> creat<strong>in</strong>g a DSS could be transferred to the
work of classical scholars (Roued-Cunliffe 2010).
One key idea beh<strong>in</strong>d the ISS is that an <strong>in</strong>terpretation is made up of a network of “percepts” that range
from low-level (determ<strong>in</strong><strong>in</strong>g a character was created by an <strong>in</strong>cised stroke) to high-level (determ<strong>in</strong><strong>in</strong>g
that several characters make up a word). While this network of percepts is implicit <strong>in</strong> the process of
papyrologists, the eSAD project plans to make them explicit <strong>in</strong> a “human-readable format through a
web-based browser application” (Olsen et al. 2009). In the application, the most elementary percepts
will be image regions that conta<strong>in</strong> “graphemes”; these images will then be divided <strong>in</strong>to cells “where
each cell is expected to conta<strong>in</strong> what is perceived as a character or a space” (Olsen et al. 2009). The
division of the image is considered to be a tessellation, and documents can be tessellated <strong>in</strong> different
ways. The basic idea is that <strong>in</strong>dividual <strong>in</strong>terpretations can be represented as “networks of substantiated
percepts” that will then be made explicit through an ontology. “The ontology aims to make the
rationale beh<strong>in</strong>d the network of percepts visible,” Olsen et al. expla<strong>in</strong>ed, “and thus expose both: (a)
some of the cognitive processes <strong>in</strong>volved <strong>in</strong> damaged texts <strong>in</strong>terpretation; and (b) a set of arguments
support<strong>in</strong>g the tentative <strong>in</strong>terpretation” (Olsen et al. 2009). The f<strong>in</strong>al ISS system will use this ontology
(that will be formatted <strong>in</strong> EpiDoc) as a framework to assist scholars <strong>in</strong> creat<strong>in</strong>g transcriptions of texts.
Another step <strong>in</strong> the model<strong>in</strong>g process for the ISS was creat<strong>in</strong>g image-capture and -process<strong>in</strong>g
algorithms that could embody perceptual processes of papyrologists. As papyrologists often do not
have access to the orig<strong>in</strong>al objects, they frequently work with digital photographs, and Tarte (2011)
acknowledged that digitiz<strong>in</strong>g wooden stylus tablets as “text bear<strong>in</strong>g objects” was not an easy feat.
Upon observ<strong>in</strong>g papyrologists, they concluded that both manipulations of the images and prior
knowledge played important roles <strong>in</strong> the perception of characters and words. The tablets were
visualized us<strong>in</strong>g polynomial texture maps, and several algorithms were used to detect the text with<strong>in</strong>
the images. The algorithms created for m<strong>in</strong>imiz<strong>in</strong>g background <strong>in</strong>terference (flatten<strong>in</strong>g the gra<strong>in</strong> of the
wood) have also been utilized <strong>in</strong> the VRE-SDM. One of the most complicated (and still ongo<strong>in</strong>g) tasks
was develop<strong>in</strong>g algorithms to extract the “strokelets” that form characters (<strong>in</strong>clud<strong>in</strong>g broken ones), as
this is the feature “on which the human visual system locks” (Tarte 2011). The f<strong>in</strong>al major algorithm
developed was a “stroke-completion algorithm” that was created to help facilitate both automatic and
scholarly identification of characters. The ISS to be developed will eventually propose potential
character read<strong>in</strong>gs (utiliz<strong>in</strong>g a knowledge base of “digitally identified list of possible read<strong>in</strong>gs”) but
will never force a user to choose one (Tarte 2011).
Many of the <strong>in</strong>sights for both the algorithm development process and the format of the ISS built off
earlier work by Melissa Terras that modeled how papyrologists read documents (Terras 2005). This
model identified various levels of read<strong>in</strong>g conducted by papyrologists (identify<strong>in</strong>g features [strokelets],
characters, series of characters, morpheme, grammatical level, mean<strong>in</strong>g of word, mean<strong>in</strong>g of groups of
words, mean<strong>in</strong>g of document), but the use of knowledge-elicitation techniques, such as “th<strong>in</strong>k aloud”
protocols, by Terras revealed that “<strong>in</strong>terpretation as a mean<strong>in</strong>g-build<strong>in</strong>g process” did not <strong>in</strong>variably
beg<strong>in</strong> at the feature level and then successively build to higher levels of read<strong>in</strong>g. Instead, as Tarte
expla<strong>in</strong>ed, the creation of <strong>in</strong>terpretations jumped between levels of read<strong>in</strong>g, and <strong>in</strong>terpretations at any
given level might <strong>in</strong>fluence those at another. Roued-Cunliffe articulated this po<strong>in</strong>t further: