Digital Assyriology—Advances in Visual Cuneiform Analysis

Abstract

Cuneiform tablets appertain to the oldest textual artifacts used for more than three millennia and are comparable in amount and relevance to texts written in Latin or ancient Greek. These tablets are typically found in the Middle East and were written by imprinting wedge-shaped impressions into wet clay. There is an increasing demand in the Digital Humanities domain for handwriting recognition, i.e., machine reading of handwritten script, focusing on historic documents. Current practice in text analysis of cuneiform script relies heavily on transliteration and translation, which are incomplete and influenced by the knowledge and experience of the expert that created them. The development of computational tools for cuneiform analysis presents many opportunities. An efficient and accurate sign spotting enables cross-referencing and statistical analyzes that are infeasible to perform manually. Furthermore, a wedge constellation spotting tool, provides experts with a significantly broader base of references to create more accurate and less time consuming transliterations and translations. Yet, cuneiform script has since resisted efforts to computational processing on basis of its basic constituents, its 3D wedge-shaped impressions and their free-form arrangements into signs. In this work, we review the literature on computational processing and recognition in the domain of cuneiform script. We introduce the different heterogeneous sources of cuneiform script, namely, manual ink-on-paper drawings, digital vector graphics drawings, photographs, and 3D scans of tablets. We describe the development of methods, beginning with the first computational classification using a hybrid manual encoding and computational comparison, to the latest methods making use of Generative Adversarial Neural Networks to recognize characters automatically. Finally, we give an overview of applications of these methods that enable quantitative mining in the small, e.g., patterns of wedge constellations, and in the large, e.g., networks of economic activity.

Transliterated Cuneiform Tablets of the Electronic Babylonian Library Platform

“…The images can be used for semi-supervised or unsupervised OCR methods (Rusakov, Somel, Fink, & Müller, 2020). For recent advances in visual methods for cuneiform script see Bogacz and Mara (2022).…”

Translating Akkadian to English with neural machine translation

“…It is important to note that the cuneiform signs used in this study are a digitized Unicode representation of sign values, as opposed to sign forms. The visual identification of the cuneiform syllabary, which will enable identifying signs on original tablets with their Unicode equivalent, remains outside the purview of the current study (for literature on visual cuneiform analysis, see ( 4 )). The OCR tool on the Babylonian Engine web application ( https://www.ben-digpasts.com/demo ) already allows to produce Unicode glyphs directly from an image of a cuneiform hand copy.…”

Cuneiform Tablets Micro-Surveying in an Optimized Photogrammetric Configuration

“…The digital replica can be helpful for many reasons and has many innovative applications: (i) it allows the virtual manipulation of these delicate findings; (ii) it allows the access and the sharing of artifact documentary information; (iii) it facilitates a collaborative interdisciplinary study among several experts; and (iv) it allows experimentation with machine learning for automatic character recognition and linguistic studies [15][16][17]. The digitization of cuneiform tablets in 3D has many positive implications for both research and 'edutainment' [18].…”