Computer Vision and Machine Learning Methods for Analyzing First Temple Period Inscriptions

220 Pages·2010·7.69 MB·English

by  Arie Shaus

The Raymond and Beverly Sackler Faculty of Exact Sciences
School of Mathematical Sciences
Department of Applied Mathematics

Computer Vision and Machine Learning Methods
for Analyzing First Temple Period Inscriptions

Thesis submitted for the degree of Doctor of Philosophy
by
Arie Shaus

This work was carried out under the supervision of Professor Eli Turkel
Secondary supervisor: Professor Israel Finkelstein

Submitted to the Senate of Tel Aviv University
December 2017

Dedicated to the blessed memory of
my late nephew Ido Gofer and my
late uncle Ilya Shaus.

Acknowledgements
I would like to express the sincerest gratitude to my supervisor, Prof. Eli Turkel,
for his thoroughly knowledgeable advices, suggestions, corrections and, no less
importantly, his kind demeanor and infinite patience, contributing to successful
culmination of this thesis. I would also like to thank my secondary supervisor, Prof.
Israel Finkelstein, for his ongoing support, thought-provoking riddles, and an
inspiration to aim at unearthing the answers to the “big questions”. Although not a
formal supervisor, Prof. Eli Piasetzky was instrumental in inaugurating and backing my
research, while pushing many of the investigations beyond their finishing line, and I’m
grateful to him.
I would like to thank the Lautman family and the Adi Lautman Interdisciplinary
Program for Outstanding Students for the wealth and scope of knowledge acquired
during my first academic steps, including the generous funding and scholarships. I’m
also thoroughly grateful to the Azrieli family and to the Azrieli Foundation for the
award of an Azrieli Fellowship, to the Foundation’s ever-positive program manager,
Ms. Rochelle Avitan, the program coordinator, Ms. Yula Panai, as well as the
Foundation’s coordinator at TAU, Ms. Shoshi Noy, for their sympathetic help. This
study was also supported by a generous donation of Mr. Jacques Chahine, made through
the French Friends of Tel Aviv University. The considerable help of Amalia Biron-
Cegla Scholarship, Early Israel Grant (New Horizons project), and Electronic Imaging
Student Grant is similarly acknowledged. In addition, the research received funding
from the Israel Science Foundation – F.I.R.S.T. (Bikura) Individual Grant no. 644/08,
the Israel Science Foundation Grant no. 1457/13, and the European Research Council
under the European Community's Seventh Framework Programme (FP7/2007-
2013)/ERC grant agreement no. 229418.
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I would also like to express gratitude to my dear longue durée friends,
confidants, colleagues, coauthors, reviewers, critics and tee-drinking associates, Shira
Faigenbaum-Golovin and Barak Sober. The cooperation with Anat Mendel-
Geberovich, in particular on character recognition, documentation and smoothing, was
ever so engaging. I’m also grateful to Yariv Aizenbud and Eythan Levy for stimulating
discussions and encouragement.
I would also like to acknowledge the valuable assistance, advice and
cooperation from Dr. Leah Bar, Ms. Yael Barschak, Dr. Shirly Ben-Dor Evian, Mr.
Michael Cordonsky, Ms. Judith Dekel, Ms. Sivan Einhorn, Ms. Noa Evron, Ms. Liora
Freud, Mr. Assaf Kleiman, Ms. Tamar Lavee, Prof. David Levin, Prof. Murray
Moinester, Ms. Ma’ayan Mor, Prof. Nadav Na’aman, Ms. Myrna Pollak, Prof.
Christopher A. Rollston, Prof. Benjamin Sass, Mr. Pavel Shargo, Prof. David M.
Steinberg, and Prof. Bruce E. Zuckerman. Your help is greatly appreciated. This
research is also in great debt to the late Prof. Itzhaq Beit-Arieh. The remarks of the
anonymous reviewers were very beneficial for the improvement of the text.
Finally, I would like to thank my family: my parents, for cultivating my
curiosity since early age; my dear sister Maria, her husband Itay, and their cute kids
Ido, Noa and Omri for little moments of happiness; my fiancée Anna for her
unconditional love and support - you undoubtedly deserve another thesis for your
perseverance in developing countless ways to encourage my work on the thesis
(!ةبوبحم اركش); and our beloved dog Hatti, who helped me finalize my ideas during our
mutual walks.
Ostraca images: courtesy of the Institute of Archaeology, Tel Aviv University
(photographer: Michael Cordonsky) and the Israel Antiques Authority. Facsimiles:
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courtesy of Ms. Judith Dekel; of the Israel Exploration Society (Aharoni 1981); and of
Prof. C. A. Rollston (Rollston 2006). Full spectrum color images of the Dead Sea
Scrolls courtesy of the Israel Antiques Authority (photographer: Shai Halevi; Dead Sea
Scrolls 2016). Paleographic tables are courtesy of the Israel Exploration Society
(Aharoni 1981).

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Abstract
The thesis concentrates on computational methods pertaining to ancient ostraca
- ink on clay inscriptions, written in Hebrew. These texts originate from the biblical
kingdoms of Israel and Judah, and dated to the late First Temple period (8th – early 6th
centuries BCE). The ostraca are almost the sole remaining epigraphic evidence from
the First Temple period and are therefore important for archaeological, historical,
linguistic, and religious studies of this era. This “noisy” material offers a fertile ground
for the development of various “robust” image analysis, image processing, computer
vision and machine learning methods, dealing with the challenging domain of ancient
documents’ analysis. The common procedures of modern epigraphers involve manual
and labor-intensive steps, facing the risk of unintentionally mixing documentation with
interpretation. Therefore, the main goal of this study is establishing a computerized
paleographic framework for handling First Temple period epigraphic material. The
major research questions, addressed in this thesis are: quality evaluation of manual
facsimiles; quality evaluation of ostraca images; automatic binarization of the
documents and its subsequent refinement; quality evaluation of binarizations on global
and local levels; identification of different writers between inscriptions (two distinct
methods are proposed); image segmentation (with improvements over the classical
Chan-Vese algorithm); and letters’ shape prior estimation. The developed methods
were tested on real-world archaeological and modern data and their results are found to
be favorable.
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