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

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The image-registration algorithm developed by Baumann and Seales was successful, and the authors
rightly concluded that:
High-resolution, multispectral digital imag<strong>in</strong>g of important documents is emerg<strong>in</strong>g as a
standard practice for enabl<strong>in</strong>g scholarly analysis of difficult or damaged texts. As imag<strong>in</strong>g
techniques improve, documents are revisited and re-imaged, and registration of these images
<strong>in</strong>to the same frame of reference for direct comparison can be a powerful tool (Baumann and
Seales 2009).
The work of the EDUCE Project illustrates how the state of the art is be<strong>in</strong>g used to provide new levels
of access to valuable and damaged manuscripts.
Lat<strong>in</strong>
In light of the extensive digitization of cultural heritage materials such as manuscripts and the large
number of Lat<strong>in</strong> texts that are becom<strong>in</strong>g available through massive digitization projects, techniques for
improv<strong>in</strong>g access to these materials is an area of grow<strong>in</strong>g research that is exam<strong>in</strong>ed <strong>in</strong> this subsection.
A variety of approaches have been explored for improv<strong>in</strong>g access to Lat<strong>in</strong> manuscripts. Leydier et al.
(2007) explored the use of “word-spott<strong>in</strong>g” to improve <strong>in</strong>formation retrieval of textual data <strong>in</strong>
primarily Lat<strong>in</strong> medieval manuscript images. They describe the technique as follows:
In practice, word-spott<strong>in</strong>g consists <strong>in</strong> retriev<strong>in</strong>g all the occurrences of an image of a word. This
template word is selected by the user by outl<strong>in</strong><strong>in</strong>g one occurrence on the document. It results <strong>in</strong>
the system propos<strong>in</strong>g a sorted list of hits that the user can prune manually. … Word-spott<strong>in</strong>g is
based on a similarity or a distance between two images, the reference image def<strong>in</strong>ed by the user
and the target images represent<strong>in</strong>g the rest of the page or all the pages of a multi-page
document. Contrary to text query on a document processed by OCR, a word-image query can
be sensitive to the style of the writ<strong>in</strong>g or the typography used. This technique is used when
word recognition cannot be done, for example on very deteriorated pr<strong>in</strong>ted documents or on
manuscripts (Leydier et al. 2007).
The authors report that ma<strong>in</strong> drawback to this approach is that a user has to select a keyword <strong>in</strong> a
manuscript image (typically based on an ascii transcript) as a basis for further image retrieval, limit<strong>in</strong>g
their approach to retrieval of other images by word only.
Another approach, presented by Edwards et al. (2004), tra<strong>in</strong>ed a generalized Hidden Markov Model
(gHMM) on the transcription of a Lat<strong>in</strong> manuscript to get both a transmission model and one example
each for 22 letters to create an emission model. Their transition model for unigrams, bigrams, and
trigrams was fitted us<strong>in</strong>g the Lat<strong>in</strong> Library’s electronic version of Caesar’s Gallic Wars, and their
emission model was tra<strong>in</strong>ed on 22 glyphs taken from a twelfth-century manuscript of Terence’s
Comoediae. In contrast to Leydier et al., the authors argued that word-spott<strong>in</strong>g was not entirely
appropriate for a highly <strong>in</strong>flected language such as Lat<strong>in</strong>:
Manmatha et al. … <strong>in</strong>troduce the technique of “word spott<strong>in</strong>g,” which segments text <strong>in</strong>to word
images, rectifies the word images, and then uses an aligned tra<strong>in</strong><strong>in</strong>g set to learn
correspondences between rectified word images and str<strong>in</strong>gs. The method is not suitable for a
heavily <strong>in</strong>flected language, because words take so many forms. In an <strong>in</strong>flected language, the
natural unit to match to is a subset of a word, rather than a whole word, imply<strong>in</strong>g that one