Authors:
Maruf A. Dhali
1
;
Sheng He
1
;
Mladen Popović
1
;
Eibert Tigchelaar
2
and
Lambert Schomaker
1
Affiliations:
1
University of Groningen, Netherlands
;
2
KU Leuven, Belgium
Keyword(s):
Dead Sea Scrolls, Handwritten Document Analysis, Digital Palaeography, Writer Identification, Handwriting Recognition, Pattern Recognition, Feature Representation, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Feature Selection and Extraction
;
Image Understanding
;
Pattern Recognition
;
Theory and Methods
Abstract:
To understand the historical context of an ancient manuscript, scholars rely on the prior knowledge of writer
and date of that document. In this paper, we study the Dead Sea Scrolls, a collection of ancient manuscripts
with immense historical, religious, and linguistic significance, which was discovered in the mid-20th century
near the Dead Sea. Most of the manuscripts of this collection have become digitally available only recently
and techniques from the pattern recognition field can be applied to revise existing hypotheses on the writers
and dates of these scrolls. This paper presents our ongoing work which aims to introduce digital palaeography
to the field and generate fresh empirical data by means of pattern recognition and artificial intelligence. Challenges
in analyzing the Dead Sea Scrolls are highlighted by a pilot experiment identifying the writers using
several dedicated features. Finally, we discuss whether to use specifically-designed shape features for writer
identific
ation or to use the Deep Learning methods on a relatively limited ancient manuscript collection which
is degraded over the course of time and is not labeled, as in the case of the Dead Sea Scrolls.
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