Digital resources in the Social Sciences and Humanities OpenEdition Our platforms OpenEdition Books OpenEdition Journals Hypotheses Calenda Libraries OpenEdition Freemium Follow us

Large Scale Wedge Extraction

Ein Beitrag von Martin Seiler, Hubert Mara, und Bartosz Bogacz

Computational tools for cuneiform analysis using 3D-datasets present numerous challenges including the detection of wedges as basic element of characters. In contrast to modeling and detecting characters as amorphous regions we propose the modeling of wedges as discrete units having relative spatial positions within symbols. Additional to the overall process being part of an Optical Character Recognition in 3D it allows to gather numerical evidence to be utilized in paleography.
An automated computational extraction of wedge-shaped impressions has proven challenging on basis of its unique properties: (i) It is a three-dimensional script that uses no surface markings, (ii) it lacks any whitespace and tokenization is dependent on content thus precluding any segmentation without a language model, and (iii) its free-form arrangement in both spatial directions, with writing evolving more than three thousand years and serving many languages, leaves few invariant patterns that could be exploited for recognition. Breakthroughs in recognition were only achieved after high-resolution 3D scanning of cuneiform tablets, extensive pre-processing methods of the resulting 3D meshes, and robust approaches to pattern matching became all widely used.
We present the extraction of wedge-shaped impressions from 3D scanned cuneiform tablets of the large open-access dataset HeiCuBeDa¹ based on the work of Fisseler². The tablets in this dataset contain extensive meta-data from the Cuneiform Digital Library Initiative (CDLI) including period of origin, literary genre, and language. Additionally, the published tablets are manually oriented to face forward with script levelled horizontally (where possible) and enriched with the noise resistant surface curvature features computed using Multi-Scale Integral Invariants (MSII) of the GigaMesh Software Framework³.
MSII robustly computes where and to what degree a local region of a surface curves. Flat regions have zero curvature, while concave and convex regions are distinguished by the sign of the computed value. We make use of the curvature to locate points of locally maximal concavity. These are deep and narrow recesses created by wedge-shaped impressions.
To further reduce noise, we perform a non-maximum suppression. Each detected local maximum is only kept for further processing if it is truly maximal in a larger, a-priori defined, radius. Then, the remaining local maxima of concavity on the tablet surface identify the deepest points of individual wedge-shaped impressions.
We determine the extents of the individual impressions by expanding a segmentation region beginning with the deepest points and growing it toward surface locations with flat surface curvature, the tablet writing surface. For very densely written script, two regions expanding side by side may collide. In that case, their border persists and the individual regions grow only where there are no other expanding regions, ensuring that constellations of overlapping wedges are not segmented as a single wedge.
Figure 1: Excerpt from cuneiform tablet HS 626 (https://doi.org/10.11588/heidicon/1110942).
(a) Tablet and wedge-shaped impressions colored by the physical color acquired during scanning.
(b) The same tablet and wedge-shaped impressions  colored by detected tetrahedron face, shown as red, green and light-red. Black lines between colored regions denote shores of vertices used to estimate tetrahedron edges.
The idealized geometrical shape of a wedge-shaped impression is a tetrahedron as shown by Cammarosano.⁴ We identify the edges and faces of a tetrahedron in an impression by estimating the parameters of the abstract model based on the MSII features of the scanned tablet. We determine the faces of the tetrahedron by grouping the surface normal (direction the surface is facing) in the previously segmented region of an individual wedge into three distinct groups with k-Means clustering.
Shores of the three distinct and adjoining groups are all points that straddle any two groups. The set of points on the shores forms three line-segments starting from the deepest point (where all three regions meet) in a star-like formation. By estimating the parameters of this abstract geometrical shape with Random Sample Consensus (RANSAC) we are able to fully describe a tetrahedron representing the wedge-shaped impression.
We evaluate our approach on test set of 10 tablets where we manually located and counted wedges providing us with a ground-truth on the contents of the tablets. By comparing our ground-truth to the automatically extracted wedges we report a recall of 96% and a precision of 58%. Given the recency of our implementation, we currently finished the extraction from 800 of the 1977 tablets of the HeiCuBeDa dataset. The data and code used for this computation are both open-access⁵ and open-source⁶.
The distinct extraction of wedges and modeling as tetrahedrons enables us to perform a statistical analysis of wedge metrics and correlate wedge volume and wedge impression deepness to tablet literary genre, period of origin and language. However, due to a large number of outliers in the identified wedges we find no significant correlations. Still, we provide distributions and charts showing the variation of wedge sizes in relation to time periods and/or genres. Together, with a planned a-priori model of feasible wedges shapes and feasible wedge n-grams we will be able to remove false positives and detect only proper wedges. Statistical analyses, as shown by this work, combined with larger amounts of tablets becoming available as high-resolution 3D datasets, will enable new investigations and insights in topics like layout detection or paleography.
Figure 2: BoxWhiskerPlot of Language versus Wedge Depth
Wedge depth is measured as the distance from the deepest vertex of a tetrahedron to its face that is level with the writing surface. Orange lines inside the boxes denote the median of the data. Box themselves contain 50% of the data values while their outer whiskers contain 98%. Black circles are data points beyond the 99th percentile.

 

¹ https://doi.org/10.11588/data/IE8CCN
²
Fisseler, “Contributions to computer-aided analysis of cuneiform tablet fragments”, Thesis 2019, Dortmund University
³ https://gigamesh.eu/
https://cuneiform.neocities.org/CWT/CWT.html
https://gitlab.com/fcgl/releases/-/tree/master/meyer_msc_2020
https://gitlab.com/fcgl/GigaMesh

 

Kurzvitae

Martin Seiler schließt im Frühjahr 2021 sein Masterstudium in Angewandter Informatik an der Ruprecht-Karls Universität Heidelberg ab.
Während seines Studiums haben ihn die Themen Computergraphik, Bildverarbeitung und Software Engineering besonders interessiert.
Er ging in den Niederlanden zur Schule und privat interessieren ihn Fremdsprachen.
Die Themen aus seinem Studium und der Bezug zu Fremdsprachen lassen sich auch in seiner Masterarbeit wiederfinden, was er als glückliche Fügung empfindet.

Bartosz Bogacz promovierte in der Informatik an der Universität Heidelberg mit dem Thema der automatischen Keilschrifterkennung und Analyse.
In nachfolgenden Projekten veröffentlichte Bartosz Bogacz in Bereichen der Computational Archaeology mit Methoden des maschinellen Lernens.
Er entwickelte neuartige Methoden, die die unstrukturierten 3D Daten gescannter Keilschrifttafeln mit neuralen Netzwerken verarbeiten.
Für 3D Scans von ägäischen Siegeln entwarf Bartosz Bogacz Registrationsalgorithmen, um die zugrundeliegende Materialdeformationen sichtbar und quantitativ untersuchbar zu machen.


OpenEdition schlägt Ihnen vor, diesen Beitrag wie folgt zu zitieren:
IDCS (23. Februar 2021). Large Scale Wedge Extraction. IDCS - Initiative for Digital Cuneiform Studies. Abgerufen am 18. März 2025 von https://idcs.hypotheses.org/248


Schreibe einen Kommentar

Deine E-Mail-Adresse wird nicht veröffentlicht. Erforderliche Felder sind mit * markiert

This site uses Akismet to reduce spam. Learn how your comment data is processed.