CNN-based Image Models Verify a Hypothesis that The Writers of Cuneiform Texts Improved Their Writing Skills When Studying at the Age of Hittite Empire

TL;DR

This study uses CNN-based image analysis to examine cuneiform tablets, revealing that writers improved their skills through study, a finding not accessible via traditional linguistic methods.

Contribution

Introduces a novel CNN-based methodology for analyzing cuneiform images, enabling insights into ancient writing practices without segmenting individual signs.

Findings

Identifies a 'teacher-student' dynamic in tablet writing

Suggests writers improved skills through study

Demonstrates CNN effectiveness in ancient script analysis

Abstract

A cuneiform tablet KBo 23.1 ++/KUB 30.38, which is known to represent a text of Kizzuwatna rituals, was written by two writers with almost identical content in two iterations. Unlike other cuneiform tablets that contained information such as myths, essays, or business records, the reason why ancient people left such tablets for posterity remains unclear. To study this problem, we develop a new methodology by analyzing images of a tablet quantitatively using CNN (Convolutional Neural Network)-based image models, without segmenting cuneiforms one-by-one. Our data-driven methodology implies that the writer writing the first half was a `teacher' and the other writer was a `student' who was training his skills of writing cuneiforms. This result has not been reached by classical linguistics. We also discuss related conclusions and possible further directions for applying our method and its generalizations.