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present an extension and comb<strong>in</strong>ation of local image descriptors relevant for ancient co<strong>in</strong>
recognition (Kampel and Zaharieva 2008)
They identified two major processes that must first be differentiated: co<strong>in</strong> identification, where a
unique identifier is assigned to a specific co<strong>in</strong>; and co<strong>in</strong> classification, where a co<strong>in</strong> is assigned to a
predef<strong>in</strong>ed type. The authors argued that automatic co<strong>in</strong> identification was an easier task because of
way <strong>in</strong> which ancient co<strong>in</strong>s were created. For example, the manufactur<strong>in</strong>g process tended to give co<strong>in</strong>s
unique shapes (hammer<strong>in</strong>g procedures, co<strong>in</strong> breakages, etc.) The same features that assist <strong>in</strong> <strong>in</strong>dividual
co<strong>in</strong> identification complicate automatic classification, however, for as they noted, “the almost
arbitrary shape of an ancient co<strong>in</strong> narrows the amount of appropriate segmentation algorithms”
(Kampel and Zaharieva 2008). Additionally, algorithms that performed well on image collections of
modern co<strong>in</strong>s did not fare well on medieval ones.
Ultimately, Kampel and Zaharieva (2008) decided to use “texture sensitive po<strong>in</strong>t detectors” and
conducted <strong>in</strong>itial experiments to determ<strong>in</strong>e what local feature descriptors would work best for
identify<strong>in</strong>g a given set of <strong>in</strong>terest po<strong>in</strong>ts <strong>in</strong> ancient co<strong>in</strong>s. After acquir<strong>in</strong>g a set of images of 350 co<strong>in</strong>
types from the Fitzwilliam Museum <strong>in</strong> Cambridge, they built a co<strong>in</strong>-recognition workflow. S<strong>in</strong>ce they
were us<strong>in</strong>g images of <strong>in</strong>dividual co<strong>in</strong>s, they did not need to automatically detect and segment co<strong>in</strong>s
found <strong>in</strong> the images and <strong>in</strong>stead focused on the feature-extraction process. That process <strong>in</strong>volved two
steps: the use of local feature algorithms to extract local image descriptors that could be used <strong>in</strong>
<strong>in</strong>dividual co<strong>in</strong> identification; and the extraction of features that could be used to “to reduce the
number of required feature comparisons” by reduc<strong>in</strong>g the co<strong>in</strong>s that needed to be extracted from the
database. After an <strong>in</strong>itial preselection step, they performed descriptor match<strong>in</strong>g by “identify<strong>in</strong>g the first
two nearest neighbors <strong>in</strong> terms of Euclidean distances.” The f<strong>in</strong>al step was verification. An algorithm
called SIFT (Scale Invariant Feature transform) provided the best results <strong>in</strong> terms of discrim<strong>in</strong>ant
feature identification, but its biggest drawback was computational time. For future experiments, they
plan to expand their evaluation to a larger set of co<strong>in</strong> images. As more collections of co<strong>in</strong>s become
available for onl<strong>in</strong>e experimentation, the accuracy and viability of such approaches will likely <strong>in</strong>crease.
Palaeography
The discipl<strong>in</strong>e of palaeography has received some brief exam<strong>in</strong>ation <strong>in</strong> other sections, such as <strong>in</strong> the
creation of a palaeographic knowledge base for Cuneiform and <strong>in</strong> terms of automatic document
recognition for Lat<strong>in</strong> manuscripts. 457 A fairly comprehensive def<strong>in</strong>ition has been offered by Moalla et
al. (2006):
The paleography is a complementary discipl<strong>in</strong>e of the philology. … The paleography studies
the layout of old manuscripts and their evolutions whereas the classic philology studies the
content of the texts, the languages and their evolutions. The goals of the palaeographic science
are ma<strong>in</strong>ly the study of the correct decod<strong>in</strong>g of the old writ<strong>in</strong>gs and the study of the history of
the transmission of the ancient texts. The palaeography is also the study of the writ<strong>in</strong>g style,
<strong>in</strong>dependently from the author personal writ<strong>in</strong>g style, which can help to date and/or to
transcribe ancient manuscripts (Moalla et al. 2006).
The study of palaeography is thus closely tied to work with many other discipl<strong>in</strong>es, and as Ciula (2009)
expla<strong>in</strong>s, “Palaeography cannot proceed without shar<strong>in</strong>g methods, tools and outcomes with codiscipl<strong>in</strong>es
such as epigraphy, codicology, philology, textual criticism—to name but a few.” As this
457 Some other computer science research <strong>in</strong> palaeography has focused on the development of automatic handwrit<strong>in</strong>g recognition for medieval English
documents (Bulacu and Schomaker 2007) and for eighteenth- and n<strong>in</strong>eteenth-century French manuscripts (Egl<strong>in</strong> et al. 2006).