Ralegh Radford Rome Awards: Restoring ancient text using machine learning: a case-study on Greek and Latin epigraphy

Sommerschield, Thea

doi:10.1017/s0068246220000240

Cited by 2 publications

(2 citation statements)

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“…Machine Learning has also been applied to textual data, both modern and ancient. Some examples of the analysis of ancient texts are the translation of cuneiform script using an app (Sanders, 2018) and the reconstruction of missing pieces of ancient Greek text (Sommerschield, 2020). But mostly, ML is used to analyse modern texts about archaeology: e.g.…”

Section: Machine Learningmentioning

confidence: 99%

Information Extraction and Machine Learning for Archaeological Texts

Brandsen

2023

Quantitative Archaeology and Archaeological Modelling

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Archaeologists are creating ever-increasing amounts of textual data. So much in fact, that manual reading and inspection has become practically impossible. By leveraging computational approaches, it is possible to extract relevant information from this big data, allowing for more efficient research and new analyses. In this chapter, methods and techniques to extract information from archaeological texts through Machine Learning are introduced and discussed, with a focus on practical examples. After reading the chapter, you should have a clear grasp on the possibilities of text mining in archaeology, the current state of research, and enough information to start your own text analyses.

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Section: Machine Learningmentioning

confidence: 99%

Information Extraction and Machine Learning for Archaeological Texts

Brandsen

2023

Quantitative Archaeology and Archaeological Modelling

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“…However, the specialists identify the photographic characters, based on their experience and intuition, which results in low efficiency and great ambiguity. Automatic photographic ancient character recognition, using deep learning methods, has great potential for accelerating the process of ancient writing research, e.g., decipherment [16,39] and restoration [1,45,61]. Nevertheless, deep learning methods require large amounts of well-labeled data to learn an accurate classifier [36,54,63,64].…”

Section: Introductionmentioning

confidence: 99%

AGTGAN: Unpaired Image Translation for Photographic Ancient Character Generation

Huang

Yang

Dai

et al. 2022

Proceedings of the 30th ACM International Conference on Multimedia

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The study of ancient writings has great value for archaeology and philology. Essential forms of material are photographic characters, but manual photographic character recognition is extremely timeconsuming and expertise-dependent. Automatic classification is therefore greatly desired. However, the current performance is limited due to the lack of annotated data. Data generation is an inexpensive but useful solution to data scarcity. Nevertheless, the diverse glyph shapes and complex background textures of photographic ancient characters make the generation task difficult, leading to unsatisfactory results of existing methods. To this end, we propose an unsupervised generative adversarial network called AGTGAN in this paper. By explicitly modeling global and local glyph shape style, followed by a stroke-aware texture transfer and an associate adversarial learning mechanism, our method can generate characters with diverse glyphs and realistic textures. We evaluate our method on photographic ancient character datasets, e.g., OBC306 and CSDD. Our method outperforms other state-of-the-art methods in terms of various metrics and performs much better in terms of the diversity and authenticity of generated samples. With our generated images, experiments on the largest photographic oracle bone character dataset show that our method can achieve a significant increase in classification accuracy, up to 16.34%. The source code is available at https://github.com/Hellomystery/AGTGAN.

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Ralegh Radford Rome Awards: Restoring ancient text using machine learning: a case-study on Greek and Latin epigraphy

Cited by 2 publications

References 0 publications

Information Extraction and Machine Learning for Archaeological Texts

Information Extraction and Machine Learning for Archaeological Texts

AGTGAN: Unpaired Image Translation for Photographic Ancient Character Generation

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