PaleoCodage—Enhancing machine-readable cuneiform descriptions using a machine-readable paleographic encoding

Homburg, Timo

doi:10.1093/llc/fqab038

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…English, German, Spanish, Arabic, etc) Lexicography Ontolex-Lexicog Apertium 22 bilingual lexicons in RDF [117] (eg. English-Spanish, English-Catalan, Occitan-Catalan, etc), Linking Latin project (LiLa) [158] in Latin Etymology and diachronicity lemonETY [136], Extension of OntoLex-Lemon with paleocodes [128] Dictionaries of historic language stages of Germanic languages [53], Multilingual and multi-alphabetica Occitan medico-botanical lexicon [10], Resources from [104] for two click languages of Southern Africa and the historic variety Old French, Resource for Italian signed language [101] Phonetics and phonology PHOIBLE model [181] The Phonetics Information Base and Lexicon (PHOIBLE) for 2186 distinct languages [181,182] Translation and terminology…”

Section: Resultsmentioning

confidence: 99%

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Multilinguality and LLOD: A survey across linguistic description levels

Gromann,

Apostol,

Chiarcos

et al. 2024

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Limited accessibility to language resources and technologies represents a challenge for the analysis, preservation, and documentation of natural languages other than English. Linguistic Linked (Open) Data (LLOD) holds the promise to ease the creation, linking, and reuse of multilingual linguistic data across distributed and heterogeneous resources. However, individual language resources and technologies accommodate or target different linguistic description levels, e.g., morphology, syntax, phonology, and pragmatics. In this comprehensive survey, the state-of-the-art of multilinguality and LLOD is being represented with a particular focus on linguistic description levels, identifying open challenges and gaps as well as proposing an ideal ecosystem for multilingual LLOD across description levels. This survey seeks to contribute an introductory text for newcomers to the field of multilingual LLOD, uncover gaps and challenges to be tackled by the LLOD community in reference to linguistic description levels, and present a solid basis for a future best practice of multilingual LLOD across description levels.

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Section: Resultsmentioning

confidence: 99%

“…From a more etymological perspective, representing ancient signs, such as cuneiform signs, as LLOD should be considered. Homburg [128] proposes an extension of OntoLex-Lemon with paleocodes to this end, which requires an SVG representation among others.…”

Section: Etymology and Diachronicitymentioning

confidence: 99%

Multilinguality and LLOD: A survey across linguistic description levels

Gromann,

Apostol,

Chiarcos

et al. 2024

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CNN based Cuneiform Sign Detection Learned from Annotated 3D Renderings and Mapped Photographs with Illumination Augmentation

Stötzner,

Homburg,

Mara

2023

2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

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Digital Assyriology—Advances in Visual Cuneiform Analysis

Bogacz

Mara

2022

J. Comput. Cult. Herit.

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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.

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PaleoCodage—Enhancing machine-readable cuneiform descriptions using a machine-readable paleographic encoding

Cited by 4 publications

References 2 publications

Multilinguality and LLOD: A survey across linguistic description levels

Multilinguality and LLOD: A survey across linguistic description levels

CNN based Cuneiform Sign Detection Learned from Annotated 3D Renderings and Mapped Photographs with Illumination Augmentation

Digital Assyriology—Advances in Visual Cuneiform Analysis

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