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

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when digitiz<strong>in</strong>g early modern books, a project needs to determ<strong>in</strong>e how much functionality users will
require from a digital facsimile and how much human <strong>in</strong>tervention will be required to create it.
In analyz<strong>in</strong>g these questions, Rydberg-Cox proposed five possible approaches: (1) image books with
simple page images; (2) image books with m<strong>in</strong>imal structural data; (3) image front transcriptions (such
as those found <strong>in</strong> the Mak<strong>in</strong>g of America 58 project) with page images that have searchable uncorrected
OCR; (4) carefully edited and tagged transcriptions (generally marked up <strong>in</strong> XML); and (5) scholarly
and critical editions. Ultimately, the project decided to create sample texts <strong>in</strong> all of these genres except
that of the scholarly critical edition because of the cost of creat<strong>in</strong>g such editions. The decision to
digitize the text, rather than just provide page images with limited OCR, raised its own issues,
<strong>in</strong>clud<strong>in</strong>g the need to manually photograph rather than scan pages and how to address characters and
glyphs that could not be represented by Unicode. They had to create a method that could be used by
data entry contractors to represent characters as they typed up texts, and the first step was to create a
catalog of all the brevigraphs that appeared <strong>in</strong> the pr<strong>in</strong>ted books and that assigned a unique entity
identifier to each nonstandard character that data entry personnel could use to represent the glyph.
In addition to this catalog, a number of computational tools were created to assist the data entry
operators:
Because the expansion of these abbreviations is an extremely time-consum<strong>in</strong>g and pa<strong>in</strong>stak<strong>in</strong>g
task, we developed three tools to facilitate the tagg<strong>in</strong>g process. These tools suggest possible
expansions for Lat<strong>in</strong> abbreviations and brevigraphs, help identify words that are divided across
l<strong>in</strong>es, and separate words that are jo<strong>in</strong>ed as the results of irregular spac<strong>in</strong>g. All three programs
can return results <strong>in</strong> HTML for human readability or by XML <strong>in</strong> response to remote procedure
call as part of a program to automatically expand abbreviations <strong>in</strong> these texts (Rydberg-Cox
2009).
Another important po<strong>in</strong>t raised by Rydberg-Cox was that while the project needed to develop tools
such as this, if such tools were shared <strong>in</strong> a larger <strong>in</strong>frastructure, they could be reused by the numerous
projects digitiz<strong>in</strong>g Lat<strong>in</strong> books. Ultimately, Rydberg-Cox concluded that this work showed that a
large-scale project that created image-front editions (e.g., us<strong>in</strong>g uncorrected data that were manually
typed to support search<strong>in</strong>g rather than uncorrected OCR) could be affordably managed. In the
workflow of his own project, Rydberg-Cox found that the most significant expense was hav<strong>in</strong>g human
editors tag abbreviations and a second editor proofread the work.
Nonetheless, Rydberg-Cox conv<strong>in</strong>c<strong>in</strong>gly argued that a certa<strong>in</strong> level of transcription is typically worth
the cost because it provides better searchability and, even more important, supports automatic
hypertext and l<strong>in</strong>k<strong>in</strong>g to dictionaries and other read<strong>in</strong>g support tools. Such tools can help students and
scholars read texts <strong>in</strong> Greek and Lat<strong>in</strong> without expert knowledge of such languages, and they are
particularly important for early modern books, many of which have never been translated.
Furthermore, Rydberg-Cox noted that larger collections of lightly edited text often reach far larger
audiences than small collections of closely edited texts or critical editions. In addition, this model does
not preclude the development of critical editions, for as long as the images and transcriptions are made
available as open content they can be reused by scholars <strong>in</strong> support of their own editions.
In contrast to us<strong>in</strong>g digitized images and typed <strong>in</strong> transcriptions, recent research reported by Simone
Mar<strong>in</strong>ai (2009) explored the use of automatic text <strong>in</strong>dex<strong>in</strong>g and retrieval methods to support
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