Reading the Papyrologist: Building Systems to Aid the Humanities Expert Keywords: Papyrology, Knowledge Elicitation, Cognitive Systems

  1. 1. Melissa Terras

    Centre for Digital Humanities - University College London, Department of Information Studies - University College London, School of Library, Archive and Information Studies - University College London

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The act of Papyrology, simply defined as obtaining “a body of knowledge …from the
study of papyri” and now taken to cover “ as a matter of convenience…all materials
carrying writing… done by a pen” (Turner 1968, p vi) is an important aspect to our
understanding of ancient societies. The reading and analysis of ancient texts can
provide a vast array of historical and linguistic information, providing an important,
often first hand, companion to archaeological and scientific evidence of sites and
peoples. Although there has been much written regarding the history of papyrology
(Pattie and Turner 1974), and the contribution the transcription of such texts has made
to both literary and non-literary classical studies (Turner 1968), the process entailed in
transcribing a text has never been made explicit. Papyrology is in essence a “self
consuming labor which leaves little or no trace of itself,” (Youtie 1963, p 11) and the
expertise of papyrologists, as with the expertise of any professional, is a valuable but
surprisingly elusive resource.

An EPSRC jointly funded project at the Department of Engineering Science, and the
Centre for the Study of Ancient Documents (CSAD), University of Oxford, was
initiated three years ago to analyse ancient texts and develop new image processing
techniques to retrieve information from small incisions in damaged surfaces, the
techniques under development being applicable to a wide variety of engineering
problems. The project is concentrating on the analysis of the Vindolanda stylus
tablets: a collection of some 200 texts discovered near a Roman Fort built in the late
80s AD near Hadrian’s Wall at modern day Chesterholm (Birley 1977; Bowman
1983). Some significant progress has been made using wavelet filtering to remove
woodgrain in images of the stylus tablets, and developing and appropriating Shadow
Stereo techniques to identify candidate writing strokes (Bowman, Brady et al. 1997;
Molton 1999); these developments were presented at ALLC 2000.

However, to be able to help the papyrologists, it is important to develop tools which
can be utilised easily and confidently by them, and also provide techniques which can
mobilise the disparate linguistic and visual knowledge accessed whilst transcribing
such a text. In order to do this, it is first important to understand the process that they
go through in analysing a text. However, experts are notoriously bad at describing
what they are expert at (McGraw and Harbison-Briggs 1989). Experts utilise and
develop many skills which become automated and so they are increasingly unable to
explain their behaviour, resulting in the troublesome “knowledge engineering
paradox”: the more competent domain experts become, the less able they are to
describe the knowledge they use to solve problems (Waterman 1986). A program of
knowledge elicitation was undertaken to identify the areas in which a computer
program could aid the papyrologists, and also how the development of such a system
would add to our understanding of the papyrology process.

This has resulted in the development of a knowledge based program, firstly to try and
make explicit the reading techniques that papyrologists use whilst deciphering such
texts (which have so far remained implicit), and secondly, to provide a tool to aid the
papyrologists in recording the recursive hypotheses they develop in the transcription
of the texts. This program incorporates lexical and visual knowledge that the
papyrologists rely on to help them read such ancient texts, and will compliment the
development of the image processing techniques, eventually contributing to the
development of a Cognitive Visual System which will aim to replicate the human
ability to combine image processing, reasoning, memory, and knowledge. (It should
be stressed that this is not an attempt to build a prescriptive “expert system” that will
automatically generate a reading of the texts, but a tool through which the
papyrologists can record the various hypotheses and reasoning generated during the
transcription of such texts.) Developing such a program requires much interaction
with the papyrologists to understand the process of reading such ancient texts,
analysis of existing linguistic sources from the same period, and development of
appropriate computer skills to undertake the building of the program.

This paper will present an overview of the project, and a demonstration of the
computer program developed so far, followed by a discussion regarding what the
development of such a program has revealed about the nature of the papyrology
process and how experts approach and read ancient texts.

Birley, R. (1977). Vindolanda, A Roman Frontier Post on Hadrian's Wall. London,
Thames and Hudson.

Bowman, A. K. (1983). The Roman Writing Tablets From Vindolanda. London,
British Museum Publications LTD.

Bowman, A. K., J. M. Brady, et al. (1997). “Imaging Incised Documents.” Literary
and Linguistic Computing 12(3): 169 - 176.

McGraw, K. L. and K. Harbison-Briggs (1989). Knowledge Acquisition: Principles
and Guidelines. London, Prentice-Hall International Editions.

Molton, N. and J. M. Brady (1999). “The Choice of Light Position for Shadow Stereo
with Inscribed Tablets.” Forthcoming.

Pattie, T. S. and E. G. Turner (1974). The Written Word on Papyrus. London, British
Museum Publications. .

Turner, E. G. (1968). Greek Papyri, An Introduction. Oxford, Clarendon Press.

Waterman, D. A. (1986). A Guide to Expert Systems. Reading, MA, Addison-

Youtie, H. C. (1963). “The Papyrologist: Artificer of Fact.” GRBS 4 (1963): 19-32.

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Conference Info

In review


Hosted at New York University

New York, NY, United States

July 13, 2001 - July 16, 2001

94 works by 167 authors indexed

Series: ACH/ICCH (21), ALLC/EADH (28), ACH/ALLC (13)

Organizers: ACH, ALLC