Draft Version . Shaping History: Advanced Machine Learning Techniques for the
Analysis and Dating of Cuneiform Tablets over Three Millennia Danielle
Kapon1*, Michael Fire2, Shai Gordin34 —Cuneiform —Diplomatics —Image
Processing —VAE —Dating
—Deep Learning —Quantitative Archaeology —Shape Analysis Cuneiform tablets,
emerging in ancient Mesopotamia around the late fourth millennium BCE,
represent one of humanity’s earliest writing systems. Historically
characterized by wedge-shaped marks on clay tablets, these artifacts provided
insight into Mesopotamian civilization across administrative, commercial,
legal, literary, and scientific domains. The traditional analysis and dating
of these tablets still mainly rely on manual, subjective appreciation of shape
and writing style. This approach, however, leads to many uncertainties in
pinpointing the exact period the tablets originated from. Recent advances in
digitization have revolutionized the study of cuneiform by enhancing
accessibility and analytical capabilities. Our research uniquely focuses on
the physical shapes of tablets as significant indicators of their historical
periods, diverging from most studies that concentrate on textual content.
Utilizing an unprecedented dataset of over 94,000 images from the Cuneiform
Digital Library Initiative (CDLI) collection, our effort applies deep learning
methods to the classification of cuneiform tablets, covering more than 3,000
years of history. By leveraging statistical, computational techniques, and
innovative generative modeling through Variational Auto-Encoders (VAEs), we
achieve substantial advancements in the automatic classification of these
ancient documents, by focusing on the tablets’ silhouette as a main predictor.
Our classification approach begins with a Decision Tree using the height-to-
width ratios of the tablets as the sole predictor which achieved 8% macro
F1-score, and culminates with a ResNet50 model, which achieved a 61% macro-F1
score for tablet silhouettes. Additionally, we introduce a novel tool-set,
powered by VAEs, to enhance the interpretability of our models and enable
researchers to explore the changes in tablet shapes across different eras and
genres. This research contributes to the fields of document analysis and
diplomatics by demonstrating the value of large-scale data analysis combined
with statistical methods, also within specific historical periods. These
telescopic and microscopic insights offer valuable tools for historians and
epigraphists, enriching our understanding of both cuneiform tablets themselves
and the cultures that produced them.

##  1 Introduction

The emergence of writing, specifically the cuneiform logo-syllabic writing
system impressed with a reed stylus on clay tablets, finds its roots in
ancient Mesopotamia, one of the few places where this occurred independently
[1, 2, 3, 4, 5]. The Sumerians, followed by the Elamites, Akkadians, Hurrians,
Hittites, and many other ancient Near Eastern cultures, used this medium to
produce a vast collection of writings, including archives, literature, and
scientific texts, many of which survive due to the durability of clay [6].
While much writing has been lost, those on durable materials like clay, metal,
and stone have withstood the harsh Middle Eastern climate [7]. Despite their
durability, clay tablets can deteriorate if not properly conserved, making
restoration challenging [8]. Spanning from the fourth to the first millennium
BCE and written in at least a dozen languages, these tablets offer crucial
insights into ancient societies across regions from Iran to Egypt, the Levant,
and Anatolia [9, 10, 11]. These cuneiform inscriptions, encompassing various
genres, serve as a window into ancient societies, their politics, history,
law, and sciences [1]. However, processing hundreds of thousands of tablets
remains a challenge due to the complexity of the content of these tablets, and
the relatively small number of experts involved [12, 13, 14].

(a) Neo-Assyrian
(ca. 911-612 BC),
divination [15]

(b) Ur III
(ca. 2100-2000 BC),
administrative [16]

(c) Middle Babylonian
(ca. 1400-1100 BC),
administrative [17]

(d) Neo-Babylonian
(ca. 626-539 BC),
scholarly or scientific [18]

Figure 1: A sample of some of the variety of shapes and sizes of cuneiform
tablets texts: (a) Neo-Assyrian; (b) Ur III; (c) Middle Babylonian; (d) Neo-
Babylonian.

Dating cuneiform tablets presents ongoing challenges. While features like clay
composition, size, shape, stylistic elements, sign forms, text type and
content, associated finds, and museum "archaeology" offer solid clues, not
many standardized methods based on measurable metrics in a large-scale
statistical distribution have been established for suggesting their
chronological sequence [19, 20, 21, 22].111Another method that produces a
measurable metric based on a large statistical distribution is archaeomagnetic
dating, which depends on whether the clay artifact was baked in antiquity.
This has been demonstrated in the analysis of 32 inscribed baked bricks from
Mesopotamia (3rd–1st millennia BCE), providing high-resolution geomagnetic
intensity data. Whereas our method is purely shape-based and has disadvantages
in the case of fragmentary preservation, some archives, cities, and areas have
consistent fragmentation states that can suggest possible dating [23]
Significant discrepancies among scholars may occur despite handling tablets
from eras that have a vast collection of dated documents [19, 21, 24];
Literary compositions, for example, some of which are extent in many copies
and in different manuscript traditions, have had their manuscripts variously
dated to a period covering almost a thousand years [25, 26, 27]. This
underscores the challenges in comprehending the evolution of the oldest
recorded written language [28].

The information age, however, has ushered in a transformative approach to this
challenge. Through advanced 2D and 3D photographic techniques, the
digitization of cuneiform tablets has led to the creation of public databases
[29, 30, 31]. This digitization paves the way for the application of cutting-
edge computer vision and machine learning techniques, revolutionizing how
scholars and researchers approach the study of these ancient texts.

Prior research in the application of computational methods for dating
cuneiform tablets has primarily focused on classifying their historical period
based on the physical shape of the signs on the tablets [32, 33], performing
Optical Character Recognition (OCR) on their text [34], or analyzing their
content and genre [35, 36, 37, 38, 39, 40]. However, these studies have left a
critical gap unaddressed: the global interpretability of the models used. It
is crucial to inquire into the domain-specific reasoning behind the models’
decisions, a task typically carried out by historians or epigraphists.
Furthermore, to our knowledge, there has not yet been a study of tablet shapes
that tackles a dataset as expansive across time and space as the one we
propose to analyze.

Our study introduces a novel approach by employing machine learning to
automatically date cuneiform documents, focusing on the physical shape of
tablets as a primary indicator of their historical period. This method aims to
reduce subjectivity, offering a measurable and interpretable framework for
determining document age, thereby enhancing the precision of dating ancient
texts. The center of our research revolves around understanding the shape-
related features that make each historical period different than the next. We
are also interested in being able to show what the typical cuneiform tablet
from a certain period, or of a certain genre, looks like, and offer digital
analysis tools.

Initially, our focus was on preprocessing 94,936 tablet images from the CDLI
catalogue [29] (period and genre distribution can be seen in Figure 2). We
applied binary masks to grayscale images to generate black-and-white versions,
effectively isolating shape-related information by eliminating extraneous
visual elements. Following this preprocessing step, we performed an
exploratory data analysis (EDA) to scrutinize the 2D geometric properties of
the masked tablets, paying particular attention to their height-width ratios.
This phase aimed to enhance our understanding of the tablets’ shapes in
preparation for subsequent classification tasks.

(a) Number of Samples per Historical Period

(b) Number of Samples per Genre

Figure 2: Number of Samples per Period and Genre

Our approach for analyzing cuneiform tablets involved multiple stages,
beginning with a tablet height-width ratio analysis to measure tablet types
over time and look into prominent statistical trends, and is the first large
scale collection of such measurements, providing a robust basis for further
analysis. We proceeded with tablet period classification through various
models, including the use of DINOv2 [41] and a basic Convolutional Neural
Network (CNN) [42], which yielded a macro F1-score to 49% for masked images
and 61% for grayscale images. A significant leap in accuracy was achieved by
fine-tuning the pre-trained ResNet50 model, tailored for shape-based
classification, achieving a macro-F1-score of 71% on grayscale images and 61%
on the masked tablets. Notably, the performance on masked images demonstrated
that approximately 86% of the essential classification information was
preserved, affirming the relevance of our focus on shape-related features.

We enhanced our analysis of cuneiform tablet shapes across different
historical periods using Variational Auto-Encoders (VAEs) [43], chosen for
their ease of training, sample diversity, and mainly - their interpretability.
These generative models allowed us to examine and categorize the unique shape
characteristics of the tablets by historical period and genre. By analyzing
the encoded representations from the VAE’s bottleneck layer vectors, we
directly observed the relationships between tablet shapes and their historical
contexts. This approach not only demonstrated the utility of VAEs in
understanding the evolution of tablet shapes across different periods and
genres but also established a robust framework for classifying and
comprehending cuneiform tablets, showing how their shapes have progressed from
one period to another, among various other applications.

We were able to show that the shape of the tablets, even when represented in
2D, holds a large portion of the information regarding cuneiform tablets
dating to historical periods. We were able to use the bottleneck layer of a
VAE to show patterns in tablet shapes and provide visual tools to describe the
average characteristics of tablets from different eras, periods, and genres,
cluster tablets, and explore similarities. (Figure 3).

(a) A widget that provides an interactive platform for users to examine a
sample tablet from selected genres and historical periods. It facilitates an
exploration of the various factors encoded in the VAE’s bottleneck layer,
shedding light on the attributes that define the stylistic and temporal
aspects of the tablet, and enhancing understanding of its historical and
cultural context.

(b) A widget that enables users to interpolate between the mean tablet
representations of two distinct periods, as captured in the bottleneck layer
of a VAE. By adjusting the slider, users can visually explore the evolution of
tablet designs across periods, highlighting the average distinguishing
features of tablet shapes between the chosen historical periods. The figure
shows a tablet consisting of 40% Old Assyrian and 60% Early Old Babylonian
tablet

Figure 3: Interactive widgets for exploring tablet characteristics and
evolution.

The key contributions presented in this paper are as follows:

  * •

We conducted a thorough shape analysis and classification over the largest
dataset of cuneiform tablets to date, spanning more than 3,000 years.

  * •

We demonstrated that 2D silhouette representations of tablet shapes hold
critical information for associating cuneiform tablets with their specific
historical periods and leveraged these findings into period classification.

  * •

We leveraged VAEs to uncover patterns within tablet shapes, aiding in
distinguishing features and facilitating the development of practical visual
aids for analysis.

  * •

We analyzed the latent features extracted by VAEs, and introduced methods for
characterizing average features across eras, periods, and genres, enabling
automated clustering and detailed comparison of tablets.

  * •

We developed two widgets to enhance cuneiform tablet analysis: one for
interactive shape analysis and dating and another for visualizing design
evolution over time, utilizing deep learning and generative modeling to offer
new insights into these ancient artifacts.

Together, these contributions provide tools for historians and epigraphists to
accelerate and refine their research on tablet dating, as well as to gain
global insights regarding the relationship between tablet shape and various
genres, periods, and places of origin.

The remainder of this paper is organized as follows: Section 2 presents
existing research on tablet-based dating, interpretability of machine learning
models, and the application of generative models for interpreting data. Next,
Section 3 details our research objectives and methodology. We begin by
outlining our core approach to tablet classification. We then introduce a
novel use of generative deep neural networks, specifically VAEs, to enhance
interpretability through analysis of the latent space. Section 4 presents the
study’s findings, and Section 5, highlights key insights into tablet dating
and the interpretability through VAEs. The paper concludes in Section 6,
summarizing our contributions and discussing future research directions.

##  2 Related Work

###  2.1 Tablet Dating

Due to the absence of contemporary palaeography manuals and the scarcity of
exact methods to measure signs, crucial data cannot be retrieved for the
numerous cuneiform records that lack clear archaeological context. The shape
of a cuneiform tablet, which is part of the systematic study of external
document features called diplomatics, is a significant feature that assists in
estimating the historical period of its creation, and can also be governed by
scholarly, social, and political factors [21, 44, 45, 46]. Different tablet
shapes (also referred to as format) may be attributed to the period of
writing, but also to the context and their genre [47, 48, 49]. For example,
Podany [50] and Walker [51] found that tablets containing contracts had
different height-width ratios according to their date. Hackl [48] identified
that tablet shapes in the Late Babylonian Tattannu archive dramatically change
when arranged by groups according to their functionality and over time (late
6th \- early 4th centuries BCE). Moreover, certain document types have a
cushion-like shape, a generally more circular form, curved corners or sides,
or a generally irregular shape [52]. These shapes are attributes that can be
seen even in texts that are partially preserved. The significance of shape
analysis in other artifacts, such as pottery, underscores the importance of
such features in comprehensively understanding the origins of artifacts and
measuring their style, both within the same historical setting
(synchronically), over time (diachronically), and across space
(geographically) [53, 54]. This recognition highlights the potential for
applying similar methodologies to analyze and classify cuneiform tablets.

###  2.2 Using Machine Learning for Tablet Dating

As far as we are aware, only one study was conducted to assist scholars in the
task of tablet dating based on cuneiform tablet images, incorporating machine
learning and mainly deep learning techniques to classify the period in which a
tablet was written or the text in the tablets. Bogacz and Mara [55] proposed
using a neural network for prediction, using the mesh 3D representation of the
tablet, over four time periods - ED IIIb, Ur III, Old Babylonian, and Old
Assyrian.

An example for a similar study in ancient Greek, is Assael and Sommerschield
et al. [56], which used an input of character embeddings, word embeddings, and
positional embeddings, as part of a transformers-based architecture for a
restoration task, which can assist with the classification task of dating. For
similar studies in other ancient languages see Sommerschield et al. [57].

These studies demonstrated a commendable capacity to categorize the tablets
based on their respective periods; However, they used a small dataset
containing a small number of periods, particularly periods with a large number
of processed texts. Furthermore, the factors that drove the models’
classification process are uncertain, and it is unclear which features were
responsible for assigning them to a specific period. Also, many assumptions
regarding dates recorded in existing datasets may be biased or based on
circular reasoning, which would in turn create issues when used for training
machine learning models.

Our study provides a significant contribution to this discussion, by
leveraging a comprehensive collection of tablets from various periods spanning
several millennia. Even though the dataset we utilize, i.e., the 2D shapes of
tablets sourced from the CDLI database, presents several challenges, such as
the uneven distribution of data points, as well as possible errors in recorded
dates, it is first and foremost used to create a large scale statistical
baseline of tablet shapes. Then, machine learning methods are used to extract
analytical features in tablet shapes, which could be used to identify
stylistic connections and potential features for tablet dating.

###  2.3 Interpretability of Machine Learning and Deep Learning models

In many studies, explainability techniques are required to comprehend the
decision-making process of Machine Learning (ML) and Deep Learning (DL)
algorithms [58]. As models become more complex, it becomes increasingly
challenging to elucidate their decisions [59]. Explainable AI (XAI) is a field
of AI solutions that seeks to reveal the internal workings of ML/DL models to
users but is also a big part of the validation process before deploying a
model to production [60, 61, 62]. The definitions of interpretability and
explainability in AI differ significantly as the field is still in its early
stages, as observed in various works reviewed by Salahuddin et al. [63].
Local Interpretable Model-agnostic Explanations (LIME) is one solution for
explaining complex models. LIME employs a locally accurate multi-regression
model to approximate the behavior of a model, working with both tabular and
imagery data [64, 65]. Shapley Additive Explanations (SHAP) is another popular
solution [66]. It employs game theory to assess the significance of each
feature in the dataset and its contribution to a coalition. Several model-
agnostic frameworks have been developed that rely on the perturbation
technique. This technique involves assessing how much a feature contributes to
the model’s prediction by observing how changes in the feature’s value affect
the prediction outcome. This approach is detailed in works by Fong et al. and
Vu et al. [67, 68, 69], where they explore how altering feature values impacts
model predictions to understand the importance and influence of each feature.
Despite being utilized for various applications and being revolutionary in the
field of XAI, the techniques above provide only a local and faithful
explanation for each sample and are unable to offer a comprehensive
understanding of the decisions made by a black-box model [70].
Our research aims to comprehend the overall characteristics contributing to
the classification of cuneiform document images, which requires using
alternative techniques.

####  2.3.1 Analysis by Synthesis

The emergence of generative models has significantly impacted both the
academic community and industry applications in recent times, specifically in
the field of computational Archaeology [53, 54, 71]. Utilizing these models,
particularly for image synthesis through CNNs, enhances our ability to reveal
the underlying semantic structures of the network, offering a deeper insight
into the defining shape characteristics that contribute to the uniqueness of
tablet production across different periods [63, 70]. To provide
interpretations, scholars aim to utilize the latent space of CNNs, which
denotes the condensed form of the input image fed into the model. The process
of disentangling representations in the latent space aims to identify the most
impactful characteristics in the data, with the expectation that these traits
will be uncorrelated with one another [72].

The last decade has seen significant advancements in generative models, with
recent progress unlocking insights into their inner workings for enhanced
interpretability. One such type of models is Generative Adversarial Networks
(GANs), published in 2014, which are a class of neural network architectures
composed of two distinct models: a generator and a discriminator. These models
engage in a game-like scenario where the generator aims to create data that is
indistinguishable from real data, while the discriminator evaluates whether
the given data is real or produced by the generator. This dynamic allows GANs
to generate highly realistic data samples [73]. Recent advancements,
particularly in variations of GANs such as StyleGAN, have showcased the
ability to disentangle and manipulate the latent space. This development
allows for greater control over the characteristics of the generated images,
enhancing the capacity to generate data with specific attributes or styles
[74, 75, 76, 77].

VAEs refine the autoencoder concept by introducing a mechanism to better
navigate the latent space, enhancing the diversity and relevance of generated
images. Unlike standard autoencoders, which may struggle with diversity due to
their reconstruction loss function, VAEs incorporate a Kullback-Leibler (KL)
divergence loss. This additional loss function measures the deviation from a
prior distribution, ensuring a more structured and continuous latent space. It
allows VAEs to not just replicate input images but to also generate new,
domain-appropriate samples by adding parameters $\mu$ and $\sigma$, creating a
probabilistic space from which to draw. This approach significantly mitigates
the issue of variability and enables the traversal between different image
classes more effectively [43, 63, 70, 78, 79].

In their initial introduction, VAEs outperformed existing models, yet further
analysis revealed a significant limitation: the entanglement of their latent
variables. This entanglement, resulting from complex interconnections within
the neural network layers, means that the latent space—where the model encodes
information—is not clearly organized. Individual variables are not easily
distinguishable, complicating efforts to understand or modify the effects of
specific features on the model’s outputs [80, 81].

This is especially important for us, as in our research we wish to be able to
give clear explanations regarding the tablet shape features leading to the
period classification. Over the years, several disentanglement techniques were
introduced to tackle this issue. Schockaert et al. [61] created VAE-LIME, a
local interpretability technique based on the ideas of LIME, but incorporating
VAEs as a part of the perturbation process. VAE-LIME showed an improved
fidelity of the local model created to interpret black-box models. Another
model called pi-VAE [82], attempted to combine latent-based and regression-
based approaches for interpreting neural population data; oi-VAE [81] focused
on non-linear explanations, offering higher expressive power, and introducing
sparsity-inducing penalty, assisting the disentanglement or the latent
representations; $\beta$-VAE [80, 83, 84], suggested an augmentation factor
$\beta$ for the KL-divergence loss function portion, and was able to uncover
multiple factors of variation in the data and produce representations that are
more comprehensive and cleanly disentangled, while outperforming alternative
models. Other works [80], analyzed the characteristics of disentanglement of
$\beta$-VAE and offered improvements to the mentioned techniques to create a
more flexible and generic framework. NVAE, introduced by Vahdat et al. [85],
showed advanced VAE models with a hierarchical, multi-scale architecture,
enhancing image quality and training stability.

Denoising Diffusion Probabilistic Models (DDPMs), published in 2021, are an
evolving frontier in generative models, with enhancements broadening their
scope from image generation to interpretability, though the latter is less
examined [86, 87, 88, 89, 90, 91, 92]. They employ Markov chains for the
incremental introduction of noise into the data and leverage U-Nets for the
denoising phase, systematically removing noise to regenerate images that
resemble the original training data. Other approaches for explainability used
Auto Encoders (AEs), and specifically VAEs for understanding the connection
between inputs and their underlying contextual features [70, 93, 94, 95, 96],
as well as manipulating the outputted images based on the latent disentangled
attributes.

####  2.3.2 The Generative Learning Trilemma

When we come to choose a generative model to assist with our main task of
analysis of cuneiform tablet shape features that contribute to its
chronological origin, we need to consider a few issues.

While GANs tend to produce photo-realistic images as output, they tend to be
hard to train due to the instability of their loss function, which is composed
of a generator loss and a discriminator loss, competing with each other [63,
70, 97]. It also lacks variability in its generated images, as it is
improbable to discover a latent value that the decoder can utilize to create
valid outputs. Despite occasionally surpassing GANs in generating high-quality
samples [98, 99], DDPMs encounter significant hurdles due to their prolonged
sampling times and challenging training processes, requiring thousands of
network evaluations [87, 90, 100]. This complexity makes them less suitable
for some real-world applications and reduces their potential for
interpretation. VAEs are also much easier to train than GANs, have higher
stability, and are more efficient; However, they lack the photo-realistic
characteristics of AE, GANs, and Diffusion models [78, 100], due to the
penalty meant to restrict the network from learning latent representations
that deviate significantly from a standard normal distribution (measured by
the addition of the KL-divergence loss to the loss function).

This issue is addressed as the "Generative Learning Trilemma" [100]; This is
because generative models struggle to excel simultaneously in three important
requirements: easy and fast training process, sampling diversity, and high
sampling quality. As of now, no model was able to perfect all three of those
key requirements. While producing high-quality images with high sampling
diversity, Diffusion models tend to be hard and expensive to train [98, 99],
hence are inapplicable for many real-life problems.

##  3 Methods

Our research explores the classification of cuneiform tablets’ chronological
periods through their shapes. Initially, we apply classification models to
assess the predictive power of tablet silhouettes for dating purposes. This
foundational analysis paves the way for our main objective: employing
generative models to synthesize tablet shapes, thus advancing period
classification by merging traditional methods with innovative analysis-by-
synthesis techniques. This synthesis-driven methodology allows us to explore
and identify defining characteristics of tablets across different historical
periods more deeply. In this section, we describe the data sources, processing
steps, methodologies, and evaluation metrics included in our research. We
highlight our integrated approach that combines direct classification with
analysis by synthesis to uncover new insights into the chronology of cuneiform
records.

###  3.1 Data Acquisition

Our work utilizes a comprehensive dataset of cuneiform tablets sourced from
the CDLI API [101]. This vast collection, spanning various museums and
collections and representing the history of writing (3350 BC - pre-Christian
era), encompasses over 360,000 digitized catalog entries. For our specific
use, we selected 94,936 artifacts equipped with both an image and a historical
period classification that was provided in the dataset by domain experts
[102]. Each image has additional information like genre and origin, enriching
our understanding of its characteristics. A sample of these tablets can be
seen in Figure 1.

The dataset encompasses a diverse range of periods, categorized into three
major eras, as can be seen in Figure 2(a):222For the sake of convenience all
periodization is estimated based on the Middle Chronology [103, 104].

  1. 1.

Early Bronze Age (3rd Millennium BCE) [104]: Uruk IV (3,500-3,200? BCE), Uruk
III (3,200?-2,900 BCE), Proto-Elamite (3,100-2,900 BCE), ED I-II (2,900-2,340
BCE), ED IIIa (2,900-2,340 BCE), ED IIIb (2,900-2,340 BCE), Ebla (3,000-2,300
BCE), Old Akkadian (2,324-2,141 BCE), Lagash II (2,130-2,110 BCE), and Ur III
(2,110-2,003 BCE).

  2. 2.

Middle-late Bronze Age (2nd Millennium BCE) [105, 106]: Early Old Babylonian
(2,019–1,794 BCE), Old Babylonian (1,794-1,595 BCE), Old Assyrian (1,972-1,720
BCE), Middle Assyrian (1,500-1,000 BCE), Middle Babylonian (1,550-1,155 BCE),
Middle Elamite (1,450-1,050 BCE), and Hittite (1,500-1,180 BCE).

  3. 3.

Iron Age (1st Millennium BCE): Neo-Assyrian (934-509 BCE), Neo-Babylonian
(625-539 BCE), Achaemenid (550-331 BCE), and Hellenistic (330-64 BCE).

These period classifications aided our supervised image classification
algorithm and the validation of our generation process. The images served as
the core data for our analysis, classification, and synthesis tasks.

###  3.2 Data Preprocessing

During the data acquisition, the scraped images are stored in a grayscale
format and resized proportionally to 512x512 pixels (adding a black background
to rectangular images) to reduce model complexity. We performed this image
preprocessing because we do not require the intricate details of the tablet,
allowing us to use images of lower resolution.

(a) A tablet featuring a color and scale bar. ED I-II (ca. 2900-2700 BC),
Administrative genre.

(b) A tablet originally photographed on a non-black background. Early Old
Babylonian (ca. 2000-1900 BC), Lexical genre.

Figure 4: Tablets addressed in the preprocessing stage of the research

To effectively isolate the shape of ancient tablets from their images for
analysis or digital archiving, our methodology involves creating binary masks
that emphasize the tablet’s silhouette. Initially, we remove irrelevant
features such as scale bars and logos, employing both manual and automated
techniques to ensure the images are free of distractions. This process is
critical as it ensures we will not get a bias due to these external elements
and allows focus on the tablet, as illustrated in Figure 4 [107].
Additionally, we exclude images that do not provide a clear view of the
tablet, particularly those taken against non-black backgrounds, as shown in
Figure 4(b) [108], to guarantee the integrity of our dataset.

We apply Gaussian blur to the grayscale images to obtain a binary image
representing the tablet shapes, followed by carefully selecting a binary
threshold.333The Gaussian blur and binarization thresholds were manually
selected and tested on several hundred random images. This process aimed to
prevent the darker areas of the tablets from turning completely black and to
ensure that adjacent sides of the tablets in each image did not touch in terms
of pixels. This preparation was essential for facilitating subsequent
analysis. This technique delineates the tablet’s outline more prominently
against its background, enhancing the contrast without compromising the
shape’s integrity. The resulting binary masks effectively highlight the
tablet’s silhouette by stripping away any textual or decorative subtleties
that could obscure the shape analysis. These masks, depicted in Figure 5,
offer a simplified yet accurate representation of the tablets’ shapes. The
preprocessing of the binary masks is executed in real time, with the output
not being stored separately. This approach ensures that the binary mask
generation process is integrated seamlessly into the workflow, allowing for
immediate analysis without the need for additional storage or handling of
preprocessed images. We opted to use binary masks of the entire tablet, which
is necessary given the complex and three-dimensional nature of the writing
surfaces of cuneiform tablets. While other studies have also utilized shape
attributes, such as those on ceramic vessels, they only utilized the vessel
profiles, as is customary in ceramic topology. In contrast, our approach
requires the full silhouette to account for the intricacies of cuneiform
tablets [53, 54].

(a) Original image from CDLI [109].

(b) Grayscale image after preprocessing.

(c) Masked image after preprocessing.

Figure 5: Illustration of the process for extracting the largest component of
a tablet.

###  3.3 Exploratory Data Analysis - Height-to-Width Ratios

Our initial investigation aimed to explore potential relationships between the
physical dimensions of tablets—specifically their width and height, excluding
depth—and their historical periods, hypothesizing that these dimensions could
offer insights into the tablets’ chronological origins. Given the absence of
actual dimensions for most tablets in the dataset, we extracted the largest
connected component from the binary masks representing each tablet and derived
the pixel height and width of the largest component, a method illustrated in
Figure 6. This approach effectively isolates the primary structure of each
tablet for more accurate dimension measurement. However, this automated
extraction process might introduce inaccuracies due to factors like the
merging of other tablet views laid down on the same image or the impact of
shadows in the images. Moreover, our dataset includes fragments from certain
periods, such as the Neo-Assyrian, which could influence the analysis results.
Our technique focuses primarily on the height-to-width ratio, as the
measurements we obtained are relative to the tablet’s proximity to the camera
and the proportions within the original image. For the analysis, we used the
Pearson Correlation Matrix, as well as different visualizations Metrics
Description.

(a) Masked image after preprocessing

(b) Largest component of the masked image

Figure 6: Illustration of the process for extracting the largest component of
a tablet: (a) Masked image after preprocessing, and (b) Largest component of
the masked image.

###  3.4 Tablet Period Classification

Our approach begins with applying traditional machine learning models to
assess the predictive utility of simple geometric ratios. Next, we utilize
more modern analyses using CNNs to harness image-based features, aiming to
improve our classification accuracy by leveraging deep learning algorithms.
Our methodology progresses methodically, allowing for a comprehensive
evaluation of physical attributes in dating the tablets, as well as the
extraction of patterns in shape stylistic features across time and space.

####  3.4.1 Evaluation Metrics for Image-Based Period Classification

In all our experiments, we worked with a dataset containing 94,936 samples. We
divided these samples into different segments: Since the models we employ
require a considerable amount of data for proper training, 80% were used for
training, 10% formed the validation set, and the remaining 10% were allocated
for the test set. This division into these proportions was decided upon
following previous work [32, 40, 53, 54] who used 80%-90% for the train set,
and 10%-20% for the test set, and research regarding dataset splits [110]. Due
to computational resources, we decided to use a single validation set to help
us effectively assess the model’s performance, refine hyperparameters, and
minimize the chance of overfitting the training data. Furthermore, the
validation set allows us to adjust our experimental setup without relying on
the test data, maintaining the integrity and objectivity of our final
evaluations. For this purpose, we used 10% of the data, leaving a test set of
10%.

To evaluate the performance of our classification models in discerning between
historical periods of ancient tablets, we utilized the following metrics:
Macro F1-score [111] that considers our highly unbalanced dataset, Precision
and Recall Scores [112], Area Under Receiver Operating Curve Score (AUC) [112]
and the Confusion Matrices.

####  3.4.2 Classic Machine Learning models

Our initial attempt was to assess whether the height-to-width ratio (that was
extracted from the masked tablets in the EDA phase) alone could serve as a
predictive feature for classifying ancient tablets into their respective
historical periods. To establish a baseline for this classification, we opted
to create a basic classification model focused exclusively on this single
feature. We utilized a Decision Tree model [113]. This decision was based on
the fact that we are dealing with a single predictor - the height-to-width
ratio, and due to the model’s ability to handle non-linear relationships that
might arise from the dataset’s characteristics.

####  3.4.3 Unsupervised Feature Extraction

For our initial exploration into more advanced models, we utilized DINOv2, a
cutting-edge, pre-trained visual transformer model noted for its capabilities
in its smaller version (vits14). Originating from the model’s introduction
[41], DINOv2 was highlighted as an excellent feature extractor due to its
proficiency in meta-learning, enabling it to form robust and generalizable
representations of visual features. This quality made it potentially suitable
for our task of classifying tablet periods and is a relatively cost-effective
solution as we used it solely for inference. Leveraging this pre-trained
model, we extracted latent features (384 features per sample) for each masked
tablet and each greyscale without further fine-tuning. The extracted features
were subsequently input into an Extreme Gradient Boosting (XGBoost) model
[114] for tablet classification. We determined the XGBoost model’s
hyperparameters using a 10-fold cross-validation process.

####  3.4.4 CNNs

Building upon our initial classification efforts, we advanced to a more
intricate analysis by incorporating image-based features through CNNs. We
aimed to verify the potential of physical shapes for determining the
chronological origins of the tablets. The architecture of our CNN model
consisted of four convolutional layers, each followed by batch normalization
layers and pooling, to improve training stability and performance. The network
also included two fully connected layers at the end, using a dropout layer,
designed to synthesize the features extracted by the convolutional layers into
predictions regarding the tablets’ historical periods. The hyperparameters
chosen for this model are a batch size of 16 samples and a learning rate of
$10^{-5}$.444The hyperparameters were chosen after a process of grid-search,
taking into consideration both validation loss and validation accuracy, as
well as our computational limitations. This model configuration was applied to
grayscale images and images of the masked tablets. This comparison will
quantify the shape’s independent contribution to period prediction. We added
early-stopping to our training to avoid overfitting. As a result, training for
the grayscale images ended after 8 epochs, and for the masked tablet images
ended after 9 epochs.

####  3.4.5 Advanced CNNs - ResNet50

In our continued efforts, we implemented a pre-trained ResNet50 model [115],
highly regarded in the field for its exceptional performance in image
classification challenges [116, 117, 118]. This deep residual network is adept
at bypassing the vanishing gradient problem, thanks to its 50-layer
architecture featuring skip connections. We fine-tuned ResNet50 on two image
types: grayscale and masked tablet images (separately).

We fine-tuned the model to predict grayscale and masked images, adjusting the
learning rate to $5\cdot 10^{-5}$ and the batch size to 16.555The batch size
and learning rate hyperparameters were selected through a grid-search method,
using a validation set to evaluate each combination of hyperparameters over 3
epochs. We determined the optimal set of hyperparameters based on a balance
between computational constraints and performance across evaluation metrics.
We used the same structure of the network for both grayscale and masked images
to isolate the effect of the change of data. We used early stopping in the
training process to prevent overfitting, with both models’ training concluding
after 4 epochs.

###  3.5 Interpretability and Exploration with VAE

In the second phase of our research, we shifted our focus towards a deeper
understanding of the intricate shape characteristics that distinguish tablets
from various historical periods. Our objective was to obtain global-level
insights into the defining features of these artifacts, surpassing the
limitations of local-focused, model-agnostic interpretability methods. To this
end, we opted for explainable generative models, prioritizing a balance
between interpretability and ease of training.

After a comprehensive evaluation, we utilized a VAE-based approach. Despite
being aware of potential compromises in output fidelity, this decision was
driven by the desire to uncover subtle, period-specific shape features, and by
previous work [53, 54]. VAEs offer a structured, probabilistic method for
generating and modeling data. These qualities make them particularly suited
for our exploration into the variability of tablet shapes across different
historical contexts, and into the latent space of tablet shape
characteristics. After consideration and several attempts, we chose to process
with the basic VAE and not use any of the methods in the sections above.
However, our implementation can still be used together with $beta$-VAE.

####  3.5.1 VAE architecture

In our VAE model designed for the analysis of ancient tablet shapes, the
encoder features five convolutional layers with 5*5 size filters that increase
in count from 32 to 256, enhancing feature extraction without pooling to
maintain spatial detail. At the heart of this model is a 12-dimensional
bottleneck, a dense latent space representation of the input images that
encapsulates their fundamental features.666We made our choice of a
12-dimensional latent space by considering both prior research [84] and the
interpretability of the data. In VAEs, there is a trade-off between the size
of the latent space and its properties. While a larger space can lead to more
accurate reconstructions, it can also make interpreting the latent factors
more challenging. Given the relative simplicity of our black-and-white data,
we started with a 16-dimensional latent space. However, we were able to
achieve good reconstruction quality without significant increases in loss
functions (details provided later) by reducing the dimensionality to 12. Mu
($\mu$) and sigma ($\sigma$), calculated within this bottleneck, define the
mean and variance of the latent space, enabling effective sampling through the
reparameterization trick. This architecture allows for the precise
reconstruction and generation of tablet shapes,

The decoder part of the VAE reverses the encoder’s process, using transposed
convolutional layers to gradually upscale the compressed latent representation
back to the original image dimensions, enabling accurate reconstruction and
novel shape generation. We trained this model over 9 epochs with a 0.0001
learning rate and batch size of 8,777The selection of these hyperparameters
resulted from a grid search, where each combination was assessed over 3 epochs
using a validation set. We chose the optimal hyperparameter set by finding a
balance between performance on various evaluation metrics and computational
limitations. and included an application of class weights, with weights
inversely proportional to the number of samples per class. This VAE
architecture adeptly reconstructs and explores the morphology of historical
tablets, demonstrating its utility for detailed shape analysis.

####  3.5.2 Evaluation Metrics and Loss Functions for the VAE model

For the VAE model, we used the same train and test set as were used for the
masked tablet ResNet50 model, since we wanted to be able to compare the
performance on the same dataset. Our VAE model was designed with a focus on
interpretability. It incorporates three main components in its loss function:

  * •

Reconstruction Loss: This component ensures the VAE’s ability to faithfully
reconstruct the original tablet images, minimizing the differences between the
actual tablets and their VAE-generated equivalents. In contrast to the
original VAE paper [43], which used Binary Cross Entropy (BCE) for the
reconstruction loss, we opted to use the Mean Squared Error (MSE), as it
produced superior results in terms of reconstruction [54, 119].

  * •

Kullback-Leibler Divergence (KL Divergence): This measures the information
loss when approximating the true data distribution with the model’s
distribution [120]. By minimizing this divergence, we ensure smooth navigation
within the VAE’s latent space, facilitating effective latent space
exploration.

  * •

Cross-Entropy Loss: To enhance the model’s classification capabilities and
address class imbalances, a weighted classification loss was integrated. This
component improves the VAE’s ability to distinguish between different
historical periods accurately [121].

To assess the effectiveness of the VAE model in classifying tablets according
to their historical periods, we employed standard evaluation metrics such as
precision, recall, macro-AUC score, and macro F1-scores. These metrics and
confusion matrices provided a detailed view of the model’s performance across
different classes and highlighted specific areas where misclassification
occurred.

####  3.5.3 VAE Bottleneck Layer Exploration

In our study, the VAE model proved instrumental, mainly through its bottleneck
layer, which provides a compressed 12-feature vector representation of each
tablet. As a generative model, the VAE moves beyond mere classification,
enabling the creation of new tablets that capture the collective
characteristics of specific periods. This function allows us to examine not
just individual tablets but also to identify period-specific and genre-
specific morphological features. Our investigation focused on utilizing the
VAE’s bottleneck layer to clearly understand the defining morphological
characteristics across different historical periods, adopting a systematic
approach to analyze the rich representations derived from this layer. Our goal
was to elucidate the complex patterns and similarities among the tablets,
aiming for a deeper comprehension of their historical and cultural contexts.
To achieve this goal, we implemented a series of analytical steps as follows:

  * •

Prediction Using XGBoost: We employed the XGBoost algorithm to classify the
VAE-encoded representations of tablets, predicting their historical periods.
The choice of XGBoost was motivated by its ability to handle unbalanced data.
We used the classification to prove that the VAE still holds predictive power
regarding tablet dating, and thus can be used for their classification
interpretability.

  * •

Extracting the Mean Tablet Representation: We derived the average latent
representations from our VAE model for tablets classified by historical
period, genre, and their intersections. This step enabled us to uncover common
visual features among the tablets, setting the stage for subsequent
visualizations and analyses. By computing these mean representations, we
facilitated the generation of "mean tablet" images within the latent space,
effectively synthesizing the archetypal tablet image for each categorized
group.

  * •

Performing Various Clustering Techniques: We employed a range of clustering
techniques, including Hierarchical clustering, on the latent representations
to identify underlying patterns and relationships. This analysis enabled us to
observe how different periods are grouped at various stages of the algorithm,
facilitating an examination of visual connections both within the same
millennium and across the entire chronological span of our dataset.

  * •

Creating Widgets to Traverse the Latent Space: We developed interactive
widgets for a dynamic exploration of the VAE’s latent space, facilitating an
engaging way to visualize morphological diversity across periods and genres
and allow exploration into the meaning of each of the entries of the
12-dimensional vector in the VAE bottleneck layer (see Figure 3(a)).

##  4 Results

The resulting models and tools visualizing the different tablet features
extracted from them, act as a series of strong microscopes and telescopes to
measure hypotheses, intended to support human expert evaluation. They aim to
propose classifications for cuneiform tablets by period and diplomatic styles
and provide aggregate insights on shape-related features across different
periods and genres. We believe that these are an initial step into more in-
depth localized case studies, within specific periods, archives, and text
types.

###  4.1 Findings of Exploratory Data Analysis

Figure 7: The height-to-width ratio across periods is segmented by the
millennium. The dashed red line indicates a ratio of height to width equal to
1, distinguishing between portrait and landscape orientations.

(a) Boxplot of the height-to-width ratio per millennium. The dashed red line
indicates a ratio of height to width equal to 1, distinguishing between
portrait and landscape orientations.

(b) KDE plot of the height-to-width ratio per millennium.

Figure 8: Analysis of height-to-width ratio per millennium.

Our exploratory data analysis reveals distinct design preferences in cuneiform
tablets across different historical periods, as seen in Figure 7. In the Ur
III period, with its well known strict bureaucracy, we observed a consistent
height-width ratio, showing a tendency for portrait shaped tablets. In
contrast, Lagash II, Middle Elamite, and the latter 1st millennium clusters
(Neo-Babylonian, Achaemenid, and Hellenistic) displayed a preference for
landscape tablets. While on the whole this could be explained by the
predominance of certain legal, economic, and administrative genres in later
periods, there is a clear statistical tendency to use more landscape format
tablets already from the Early Old Babylonian period onwards; with the Old
Babylonian period, namely the first dynasty of Babylon, being a clear outlier
group.

Grouping the tablets by era reveals that those tablets from the 3rd millennium
BCE exhibit more consistency in their height-width ratio, often being
portrait-shaped, as shown in Figure 8.888We used a Kernel Density Estimation
plot (KDE plot), that visualizes the distribution of data over a continuous
interval, offering a smoothed representation of the dataset’s density.
Analyzing the Pearson correlation 9 between height and width revealed a
positive linear relationship in certain periods, with some showing
correlations above 60%. However, other periods exhibited weak correlations,
indicating a more complex relationship between tablet dimensions and their
historical context. While the majority of genres showed a correlation
exceeding 40%, the significant imbalance in the dataset across different
genres limited further genre-based analysis.

Figure 9: Pearson Correlation of height vs. width per period.

###  4.2 Tablet Period Classification Models

The application of the Decision Tree model, utilizing only the height-to-width
ratio of the tablets, yielded a macro-F1 score of 8%. This extremely low score
highlights the challenges in using the height-to-width ratio as the sole
feature for accurately classifying the tablets into their historical periods
and underscores the necessity for integrating more sophisticated and
comprehensive shape-related features into the classification model.
Interestingly, the model was not able to learn at all when it came to the
grayscale tablets, scoring a macro-F1 score of only 2%.

Using the features extracted from DINOv2, the XGBoost model achieved a macro
F1-score of 41% for the masked tablet images, marking a significant
improvement over the previous Decision Tree model. Considering that DINOv2 had
never been exposed to our dataset before, this performance was quite
remarkable. We then proceeded with additional classification models that had
been directly trained on our specific dataset.

The deployment of our CNN model on the classification task yielded a Macro
F1-score of 61% for grayscale images and 49% for silhouette images of the
tablets. These results substantially surpass the performance of initial models
that used only the height-to-width ratio for classification. Significantly,
certain periods showed exceptional model performance given only the masked
tablet images as input; the Ur III period reached a Macro F1-score of 84%, and
the Neo-Assyrian period had a remarkable 92% Macro F1-score.

When fine-tuned on grayscale images, ResNet50 achieved a macro average
F1-score of 71%. The confusion matrix, shown in Figure 10(a), reveals slight
confusion between closely related historical periods, but overall a low level
of misclassification.

(a) Grayscale Images

(b) Masked Images

Figure 10: Confusion matrices comparing the classification performance of
ResNet50 on different types of images

The performance on masked images remained solid, with the model achieving a
61% Macro F1-score. The confusion matrix for the masked images, depicted in
Figure 10(b), indicates a challenge in differentiating certain periods, such
as the Old Babylonian. It is worth noting that the ResNet50 model showed
remarkable accuracy in distinguishing specific periods from masked tablet
images only, achieving F1 scores 77% F1 score for the Old Assyrian period, 87%
F1 score for the Ur III period, and 95% F1 score for the Neo-Assyrian period.

Table 1 compiles the results from the different models used, with a note that
classes with fewer than ten instances in the test set were aggregated into an
"Other (?)" category for clarity.

Table 1: Tablet Dating - Results Summary Models | Metrics
---|---
| Macro-Precision | Macro-Recall | Macro-OvR AUC | Macro-F1
| Grayscale | Masked | Grayscale | Masked | Grayscale | Masked | Grayscale | Masked
Decision Tree | - | 0.11 | - | 0.15 | - | 0.64 | - | 0.08
DINOv2-vits14 | 0.04 | 0.63 | 0.06 | 0.36 | 0.54 | 0.88 | 0.02 | 0.42
CNN | $0.74$ | $0.61$ | $0.54$ | $0.44$ | $0.93$ | $0.88$ | $0.61$ | $0.49$
ResNet50 | 0.77 | 0.70 | 0.68 | 0.56 | 0.97 | 0.93 | 0.71 | 0.61

###  4.3 VAE Results

Following the initial classification results, we sought to interpret the
underlying characteristics responsible for the model’s predictions. To achieve
this, we employed a VAE neural network.

Figure 11: Confusion Matrix - VAE encodings classification using XGBoost.

####  4.3.1 VAE Bottleneck Period Classification

The deployment of our VAE model has provided valuable insights into the
connections between tablet shapes and their historical periods, with an
overall macro F1-score of 43%, and a macro AUC score of 88%. Classification
outcomes were achieved via an XGBoost model, as detailed in Appendix Metrics
Description, showcasing the model’s adeptness at conserving critical
classification information within a 12-dimensional bottleneck representation.
Impressively, the model scored Macro F1-scores of 79% for the Ur III category
and 92% for the Neo-Assyrian category, indicating its predictive capability
for these periods. Further examination of the confusion matrix (Figure 11)
uncovers significant confusion mainly between the Ur III, Old Babylonian, and
the rest of the classes.

####  4.3.2 Analysis of the VAE Bottleneck

By examining the mean encoding (12-features retrieved from the bottleneck
layer of the VAE) for each period999We obtained the average tablet
representation for each group by retrieving the bottleneck layer from the VAE,
averaging the 12-entry vectors, and passing the mean vector through the VAE
decoder. (Figure 16) we can potentially uncover shared visual traits and
features that differentiate tablets across historical eras. This approach
unlocks a deeper understanding of tablet shapes and their connection to
specific periods.

Additionally, analyzing genre-specific average tablets
(Figure 17) allows us to expose shape trends within genres, opening new
avenues for understanding historical trends and potentially distinguishing
tablets based on both period and genre.

Following the generation of mean tablets for each period and genre, we
utilized hierarchical clustering to explore their relationships. Specifically,
we analyzed each distinct genre group individually, using their mean period
vector representations as the clustering input.

Figure 12: Dendrogram of Mean Period Tablet - Legal Genre. The colors
represent the clusters determined by the Hierarchical clustering. Figure 13:
Dendrogram of Mean Period Tablet - Administrative Genre. The colors represent
the clusters determined by the Hierarchical clustering.

Hierarchical clustering of tablets from the Legal genre (Figure 12) shows
chronological clustering, where periods close in time, like the Early Old
Babylonian and Old Babylonian periods, cluster together. This pattern is
consistent, with a notable cluster combining periods mostly from the 2nd
millennium BCE, and a similar pattern observed in the 1st millennium BCE with
the Neo-Babylonian and Achaemenid periods. In the Administrative genre (Figure
13), tablets with round shapes cluster together, irrespective of their era. A
distinct cluster includes tablets photographed differently, where the top view
is positioned at the bottom of the image.

We continued our analysis by producing separate dendrograms per millennium, to
get a closer look at the similarities in each era, as can be seen in Figure 18
which shows the Administrative genre dendrograms, divided by millennium. We
can see nicely, how similar-shaped average-tablets are clustered together, and
how close chronological periods are clustered together like the Middle
Babylonian and the Middle Assyrian periods, and Old Babylonian and Early Old
Babylonian which were clustered together.

We conducted a deeper analysis of the mean tablets by exploring the individual
components within their 12 feature vector representations retrieved from the
VAE. Initially, we plotted the values of various entries for each period, as
illustrated in Figure 14. This plot reveals that the Middle Elamite, ED I-II
and Neo-Assyrian periods exhibit higher values for this particular entry,
whereas the Achaemenid period, tends to maintain lower values for the same
entry. This entry seems to be in charge of the way the different angles of the
tablet were placed - is the top view of the tablet presented on the top or the
bottom of the image. Entry no. 8 of the latent vector seems to be in charge of
how thick is the tablet from a side view, and entry no. 9 seems to be in
charge of how thick the tablet is from the top/bottom.

Figure 14: Summary of VAE bottleneck features: Focus on the 12th entry of the
bottleneck vector across different periods.

As we wish to provide tools for researchers to further dive into the meaning
of the different VAE components, thus understanding the important factors in
the cuneiform tablet shape-based tablet dating, we provided two visual
widgets.

The first widget showcases a random tablet from a selected period and genre,
treating each component of the tablet’s VAE bottleneck representation as a
control knob. As demonstrated in Figure 3(a), this enables the user to adjust
the value of a specific element while keeping the rest unchanged.101010Once a
user changes a value for one of the features, a new vector is created, and
passed through the VAE decoder, which generates an appropriate tablet.
Although each element influences multiple visual features, certain latent
features play more distinctive roles and can be better explained.

The second widget, illustrated in Figure 3(b), permits users to navigate the
latent space generated by the VAE between two mean tablets from chosen
periods. Users can modify the interpolation factor to select their position
along the linear combination, offering insights into the evolution of tablet
shapes over time or facilitating comparisons of tablets from identical
chronological periods in different regions.

##  5 Discussion

Improving the standardization of the process of dating cunei-form tablets
enhances our understanding of ancient civilizations, responding to the
increasing interest prompted by digitization projects that make these
artifacts widely accessible. Our study introduces a novel approach for
classifying cuneiform tablets by historical periods through shape analysis,
employing advanced machine-learning techniques to aid archaeologists and
historians. This method is designed to complement expert analysis by
uncovering insights into the design and historical context of the tablets,
particularly how their shapes reflect their functions and the societies that
created them. To achieve our goals, our work was structured in several stages:

First, our exploration of the dataset revealed its diversity, with most
tablets adopting a "fat-cross" shape. However, we observed deviations from
this general pattern, particularly in specific periods and genres. Notably,
during the Lagash II period, artifacts within the Royal/Monumental genre
primarily consisted of cylindrical shapes, unlike the common tablet forms.
Similarly, the Neo-Assyrian period was characterized by numerous small tablet
fragments. These variations pose challenges for tracking the evolution of
tablet designs and comprehending their historical and cultural significance.

Additionally, we identified discrepancies in how some tablets were
represented, primarily due to the positioning of tablet angles in images on a
single canvas during dataset curation. We also learned that our dataset is
unbalanced, with over 75% of the samples belonging to four classes and over
45% of the tablets belonging to the Administrative genre (as can be seen in
Figures 2(a) and 2(b)). This observation underscored the necessity for a
standardized approach to collecting tablet images, as well as the importance
of excavating and digitizing as many tablets from as many periods as possible.
Standardization and variability would enable more accurate analysis and
minimize potential biases impacting automated shape analysis methods.

Second, we focused on the height-to-width ratio of the tablets to get initial
conclusions regarding the shape contribution to the tablet period. We realized
that within specific periods, this ratio shows remarkable consistency,
suggesting standardized production methods reflective of the period’s
administrative and cultural practices. For instance, during the Uruk IV, ED
IIIa, Ur III, and Achaemenid periods, we observe a high degree of uniformity
in the height-width ratios of tablets. On a broader scale, our dataset
suggests a general trend toward a consistent overall shape across all periods,
demonstrating a commonality in design preferences or functionalities
throughout history, normally choosing quadrilateral-shaped tablets. However,
we could not detect a consistent overall trend across all periods, which
underlines the complexities of tablet design evolution, hinting at the
interplay of various factors— technological advancements, changes in
administrative needs, or shifts in cultural values—that influenced tablet
shape and size.

Using solely the height-to-width ratio in a Decision Tree model to classify
cuneiform tablets by historical period has shown that while these basic
measures can provide some insights, they are not sufficient for detailed
analysis, as using them in a Decision Tree only scored 8% macro F1-score.
Incorporating more advanced shape features into our models significantly
improved our ability to distinguish between periods, offering a more accurate
and intricate understanding of the tablets’ design and historical context.

Third, we employed advanced machine learning and deep learning techniques to
evaluate the effectiveness of two-dimen-sional representations in classifying
cuneiform tablets into periods. Our focus was particularly on the extent to
which silhouette representations, created using black and white masks, could
encapsulate essential information for this classification task. Among the
models we tested, ResNet50 demonstrated superior performance, showing strong
accuracy for the most frequently occurring classes and providing satisfactory
results overall. This suggests that while shape is a significant factor in
dating tablets, it is not the sole determinant. Through the analysis of
confusion matrices, we observed that some tablets from the same millennium or
geographical area often were misclassified as one another, indicating the
persistence of design traditions within these groups. In contrast, periods
characterized by unique and innovative design features were more distinctly
identifiable.

Fourth, we used VAEs to extract 12 features from each tablet’s silhouette
image through the bottleneck layer. This method allowed us to classify tablets
into their historical periods using just these 12 features and to gain new
insights into the distinct characteristics of tablets from various historical
periods. When we applied an XGBoost model to these VAE-generated features, we
expected a lower performance compared to ResNet50 because VAEs focus on
different loss functions and are traditionally unsupervised models.
Nonetheless, for major classes in our dataset, such as the Neo-Assyrian and Ur
III periods, the model achieved impressive scores. The results underscore the
VAE’s potential as a tool for data representation in shape-related
classification tasks, especially in cuneiform tablet studies.

Fifth, we utilized the latent vectors generated by the VAE for aggregative
visualizations and analyses across different historical periods. An
interesting finding was observed in the hierarchical clustering dendrogram
that was created based on the confusion matrix from the XGBoost model trained
on these VAE latent vectors (Figure 15). This analysis showed a close
classification among classes related to Babylonia, indicating similarities in
their features. In contrast, the Neo-Assyrian and Lagash II periods were
distinctly separated, likely due to their unique shapes compared to the rest
of the dataset.

Sixth, we calculated the average tablet shape for each period and genre, as
well as for combinations thereof, and conducted various analyses. These
analyses demonstrated that genres such as Administrative and Legal tended to
maintain consistent shapes across different periods, as shown in Figure 17,
which suggests a correlation between their functions and visual identities. On
the other hand, genres like Royal/Monumental, Private/Votive, and letters
displayed more variation in their average representations, indicating a more
specific relationship with shape over time. Similarly, when calculating the
mean tablet per period, the Ebla, Neo-Assyrian, and Middle Elamite periods
exhibited high shape variability, as depicted in Figure 16.

Figure 15: Dendrogram - VAE encodings of masked images, (Over confusion
matrix), showing which classes were more often confused during training.

Seventh, we also performed hierarchical clustering of the average shapes of
tablets, filtering the analysis by genre. The clustering results within the
Legal genre indicated a continuity in legal practices over closely related
periods, particularly evident from the 2nd to the 1st millennium BCE. The
dendrogram for the Administrative genre reveals that the formation of certain
clusters is influenced by the representation of the tablet images, notably the
orientation with the top view of the tablet placed at the bottom. This
indicates that the clustering algorithm may be picking up on features related
to the image orientation rather than the inherent shape of the tablets
themselves. Additionally, the dendrogram shows that tablets from the same
millenniums tend to cluster together. This could suggest that there are shared
characteristics or styles in tablet design within these time frames,
reflecting continuity in administrative practices or record-keeping
conventions across those periods.

Lastly, we developed two interactive widgets. These tools are designed to
enable researchers to conduct a more specific investigation into the evolution
of tablet shapes between different periods and to explore the meaning of each
feature within the 12-feature latent vector derived from the VAE.

##  6 Conclusions

Cuneiform tablets, the earliest form of writing, provide insights into ancient
socio-economic, legal, and cultural practices. The evolution of their designs
reflects technological progress, administrative changes, and shifts in
cultural values. Recent digitalization efforts have enabled open access to
these tablets, shifting analysis from manual to statistical methods and
enabling a broader, more efficient examination.

Our research aims to enhance the accuracy of classifying cuneiform tablets by
historical period through shape analysis, employing advanced machine learning
techniques to assist archaeologists and historians. This approach does not
seek to replace human expertise but to complement it by revealing less
apparent insights into tablet designs and their historical contexts.

We focus on using ResNet50 for shape-based dating and VAE for feature
extraction and analysis, selected for their ability to recognize subtle
characteristics of tablets from different periods. Our dataset includes images
of tablets spanning various eras, representing a significant advancement in
the field.

By moving towards automated statistical analysis, our study proposes a new
methodology for analyzing cuneiform tablets, standardizing artifact dating,
and exploring the relationship between tablet shape and format and its
historical significance. Despite challenges like dataset imbalances and
irregular tablet shapes, our work marks a considerable contribution to
historical and archaeological research, enriching the toolkit available for
studying ancient civilizations through computational methods.

##  7 Code and Data Availability

The data and code used in this study are available from the authors upon
request.

## Acknowledgments

We want to thank Ben-Gurion University of the Negev and the Department of
Software and Information Systems Engineering for the resources that were
instrumental in conducting this research. We are also thankful to Morris Alper
for his role in initiating this project and for his support in the initial
phase.

Additionally, we acknowledge the assistance of generative large language
models [122, 123], which were used to enhance the clarity and richness of the
text in this paper.

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## Appendix

### Metrics Description

In this section, we delve deeper into the metrics utilized to evaluate our
models:

  * •

Precision and Recall Scores: These scores are examined individually for each
category to understand the model’s performance per period, identifying which
categories are more challenging to classify accurately.

  * •

Macro F1-Score: This metric offers a balanced evaluation of the model’s macro-
precision (the accuracy of identified classifications) and macro-recall (the
model’s capacity to identify all pertinent instances) scores, with an average
taken across all classes. Importantly, it does not consider the number of
samples within each class, which helps us address potential biases toward the
larger classes in our unbalanced dataset.

  * •

Area Under the Receiver Operating Characteristic Curve (AUC): The AUC score is
measured as the area under the receiver operating characteristic (ROC) curve.
it is utilized to compare different models by quantifying a model’s ability to
distinguish between categories. We used the score in a one-vs-all manner for
multiclass classification. It’s worth noting that this measurement is biased
towards the larger classes in the dataset.

  * •

Confusion Matrices: To gain further insights into the model’s classification
behavior, confusion matrices are used. These matrices reveal patterns in
misclassification by showing the confusion between categories and providing a
visual representation of classification errors.

### VAE representations of the mean tablets

Figure 16: Mean tablet by period, including the number of samples per period.
Figure 17: Mean tablet by genre, including number of samples per genre.

### Dendrograms from Hierarchical Clustering over VAE latent vectors -
Administrative genre

(a) 1st Millennium BCE

(b) 2nd Millennium BCE

(c) 3rd Millennium BCE

Figure 18: Dendrograms of hierarchical clustering by millennium for tablets in
the Administrative genre. The colors represent the clusters determined by the
hierarchical clustering.