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

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One major project that has recently been funded <strong>in</strong> this area is “New Technology for Digitization of
Ancient Objects and Documents,” a jo<strong>in</strong>t project of the Archaeological Comput<strong>in</strong>g Research Group
(ACRG) and the School of Electronics and Computer Science (ECS), Southampton; the Centre for the
Study of Ancient Documents (CSAD), Oxford; the CDLI, Los Angeles-Philadelphia-Oxford-Berl<strong>in</strong>;
and the Electronic Text Corpus of Sumerian Literature (ETCSL), Oxford. 42 This project has received a
12-month Arts and Humanities Research <strong>Council</strong> (AHRC) grant to “develop a “Reflectance
Transformation Imag<strong>in</strong>g (RTI) System for Ancient Documentary Artefacts.” The team plans to
develop two RTI systems that can be used to capture high-quality digital images of documentary texts
and archaeological materials. The <strong>in</strong>itial test<strong>in</strong>g will be conducted on stylus tablets from V<strong>in</strong>dolanda,
stone <strong>in</strong>scriptions, L<strong>in</strong>ear B, and cuneiform tablets.
Other relevant research is be<strong>in</strong>g conducted by the IMPACT (Improv<strong>in</strong>g Access to Text) 43 project. The
European Commission has funded this project and it is explor<strong>in</strong>g how to develop advanced OCR
methods for historical texts, particularly <strong>in</strong> terms of the use of OCR <strong>in</strong> mass digitization processes. 44
While their research is not specifically focused on develop<strong>in</strong>g techniques for classical languages, Lat<strong>in</strong>
was the major language of <strong>in</strong>tellectual discourse <strong>in</strong> Europe for almost a century, so techniques adapted
for either manuscripts or early pr<strong>in</strong>ted books would be useful to classical scholarship and beyond.
Ancient Greek
Only a limited amount of work has considered us<strong>in</strong>g automatic techniques <strong>in</strong> the optical recognition of
ancient or classical Greek. While some recent research has focused on the development of OCR for
“Old Greek” historical manuscripts, 45 little work has explored develop<strong>in</strong>g techniques for either
manuscripts or pr<strong>in</strong>ted editions of Ancient Greek texts.
Some prelim<strong>in</strong>ary work <strong>in</strong> develop<strong>in</strong>g an automatic-recognition methodology for Ancient Greek is
detailed by Stewart et al. (2007). In these authors’ <strong>in</strong>itial survey of Greek editions, they found that on
average almost 14 percent of the Greek words on a text page were found <strong>in</strong> the notes or apparatus
criticus. The authors first used a multi-tiered approach to OCR that applied two major post-process<strong>in</strong>g
techniques to the output of two commercial OCR packages, ABBYY F<strong>in</strong>eReader (8.0) 46 and
Anagnostis 4.1. Dur<strong>in</strong>g this experiment, they found that character accuracy on simple uncorrected text
averaged about 98.57 percent. Other prelim<strong>in</strong>ary experiments with OCR-generated text revealed that
the uncorrected OCR could serve as searchable corpora. Even when work<strong>in</strong>g with a mid-n<strong>in</strong>eteenthcentury
edition of Aristotle <strong>in</strong> a nonstandard Greek font, searches of the OCR-generated text typically
provided superior recall than searches of texts that had been manually typed because the OCR text
<strong>in</strong>cluded variant read<strong>in</strong>gs found <strong>in</strong> the notes. In a second experiment, the automatic correction of s<strong>in</strong>gle
texts was performed us<strong>in</strong>g a list of one million Greek words and the Morpheus Greek morphological
analyzer that was developed by the PDL.
For their third experiment, Stewart and colleagues used the OCR output of multiple editions of the
same work to correct one another <strong>in</strong> a three-step process. First, different editions of a text were aligned
by f<strong>in</strong>d<strong>in</strong>g unique str<strong>in</strong>gs <strong>in</strong> each. Second, if an error word was found <strong>in</strong> one text, a fuzzy search was
performed <strong>in</strong> the aligned parallel text to try to locate the correct form. F<strong>in</strong>ally, once error words <strong>in</strong> a
base text had been matched aga<strong>in</strong>st potential ground truth counterparts <strong>in</strong> the parallel texts, rules
42 http://www.southampton.ac.uk/archaeology/news/news_2010/acrg_dedefi_ma<strong>in</strong>.shtml
43 http://www.impact-project.eu/home/
44 For a recent overview of some of the IMPACT project’s research, see Ploeger et al. (2009).
45 For an example, see Ntzios et al. (2007).
46 http://www.abbyy.com/