Review of Computational Epigraphy
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Abstract (arXiv)
Computational Epigraphy refers to the process of extracting text from stone inscription, transliteration, interpretation, and attribution with the aid of computational methods. Traditional epigraphy methods are time consuming, and tend to damage the stone inscriptions while extracting text. Additionally, interpretation and attribution are subjective and can vary between different epigraphers. However, using modern computation methods can not only be used to extract text, but also interpret and attribute the text in a robust way. We survey and document the existing computational methods that aid in the above-mentioned tasks in epigraphy.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 68 references that are in the map. First posted on arXiv 3 Jun 2024.
Builds on
7 of 68 references.
References · 7
- 2019Restoring ancient text using deep learning: a case study on Greek epigraphycited by 25
- 2019KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition with Deep Learningcited by 13
- 2020Restoration of Fragmentary Babylonian Texts Using Recurrent Neural Networkscited by 11
- 2021Restoring and Mining the Records of the Joseon Dynasty via Neural Language Modeling and Machine Translationcited by 9
- 2021Filling the Gaps in Ancient Akkadian Texts: A Masked Language Modelling Approachcited by 8
- 2017Deep Learning the Indus Scriptcited by 2
- 2019A Novel Approach to OCR using Image Recognition based Classification for Ancient Tamil Inscriptions in Templescited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.