2D macro-XRF to reveal redacted sections of French queen Marie-Antoinette secret correspondence with Swedish count Axel von Fersen

Michelin, Anne; Pottier, Fabien

doi:10.1126/sciadv.abg4266

Cited by 7 publications

(3 citation statements)

References 46 publications

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“…In the latter field, solutions were proposed, for example, for the recognition of the old Kuzushiji Japanese writing style [16,17], Maya [18] and Egyptian hieroglyphs classification [19,20]. These applications can be a very challenging test for CNNs, due to the complexity of the problem deriving from the artifacts' state of conservation or the high variability of documents [21][22][23]. Some of these challenges will be discussed later in this article.…”

Section: Introductionmentioning

confidence: 99%

Egyptian Hieroglyphs Segmentation with Convolutional Neural Networks

et al. 2023

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The objective of this work is to show the application of a Deep Learning algorithm able to operate the segmentation of ancient Egyptian hieroglyphs present in an image, with the ambition to be as versatile as possible despite the variability of the image source. The problem is quite complex, the main obstacles being the considerable amount of different classes of existing hieroglyphs, the differences related to the hand of the scribe as well as the great differences among the various supports, such as papyri, stone or wood, where they are written. Furthermore, as in all archaeological finds, damage to the supports are frequent, with the consequence that hieroglyphs can be partially corrupted. In order to face this challenging problem, we leverage on the well-known Detectron2 platform, developed by the Facebook AI Research Group, focusing on the Mask R-CNN architecture to perform segmentation of image instances. Likewise, for several machine learning studies, one of the hardest challenges is the creation of a suitable dataset. In this paper, we will describe a hieroglyph dataset that has been created for the purpose of segmentation, highlighting its pros and cons, and the impact of different hyperparameters on the final results. Tests on the segmentation of images taken from public databases will also be presented and discussed along with the limitations of our study.

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Section: Introductionmentioning

confidence: 99%

Egyptian Hieroglyphs Segmentation with Convolutional Neural Networks

et al. 2023

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“…X-ray phase-contrast was used by Mocella et al [3] at the European Synchrotron Radiation Facility for a computed tomography of papyrus rolls that were carbonised during the eruption of Mount Vesuvius in 79 AD. In 2021, Michelin et al [4] used X-ray fluorescence spectroscopy to distinguish different iron gall inks by determining traces of various metal elements contained in the inks. By this, a redacted part of a letter written by Marie-Antoinette during the French Revolution was recovered.…”

Section: Introductionmentioning

confidence: 99%

X-ray Dark-Field Imaging for Improved Contrast in Historical Handwritten Literature

et al. 2022

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If ancient documents are too fragile to be opened, X-ray imaging can be used to recover the content non-destructively. As an extension to conventional attenuation imaging, dark-field imaging provides access to microscopic structural object information, which can be especially advantageous for materials with weak attenuation contrast, such as certain metal-free inks in paper. With cotton paper and different self-made inks based on authentic recipes, we produced test samples for attenuation and dark-field imaging at a metal-jet X-ray source. The resulting images show letters written in metal-free ink that were recovered via grating-based dark-field imaging. Without the need for synchrotron-like beam quality, these results set the ground for a mobile dark-field imaging setup that could be brought to a library for document scanning, avoiding long transport routes for valuable historic documents.

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“…Application of cutting-edge technologies and advanced theories to the study of cultural heritage is a rapidly evolving frontier ( 12 , 13 ). Artificial intelligence was used by a team of electrical engineers and mathematicians to separate X-ray images of the Ghent Altarpiece ( 14 ).…”

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confidence: 99%

Uncovering eco-friendly design in the ancient bronze goose-and-fish lamp: an unnoticeable gap boosts ventilation

Xiao

Shen

Chen

2022

Proc. Natl. Acad. Sci. U.S.A.

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The bronze goose-and-fish lamp exhibited in the national museum of China is a 2,000-y-old artifact once used for indoor lighting by nobility in the Western Han dynasty (206 BCE TO 25 CE). The beauty of this national treasure arises from its elegant shape vividly showing a goose catching fish with beautiful colors painted over the whole body. Beyond the artistic and historical value, what enchants people most is the eco-design concept of this oil-burning lamp. It is widely believed that the smoke generated by burning animal oil can flow into the goose belly through its long neck, then be absorbed by prefilled water in the belly, hence mitigating indoor air pollution. Although different mechanistic hypotheses such as natural convection and even the siphon effect have been proposed to qualitatively rationalize the above-claimed pollution mitigation function, due to the absence of a true scientific analysis, the definitive mechanism remains a mystery. By rigorous modeling of the nonisothermal fluid flow coupled with convection-diffusion of pollutant within and out of the lamp, we discover that it is the unnoticeable gap between goose body and lamp tray (i.e., an intrinsic feature of the multicompartmental design) that can offer definitive ventilation in the lamp. The ventilation is facilitated by natural convection due to oil burning. Adequate ventilation plays a key role in enabling pollution mitigation, as it allows pollutant to reach the goose belly, travel over and be absorbed by the water.

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2D macro-XRF to reveal redacted sections of French queen Marie-Antoinette secret correspondence with Swedish count Axel von Fersen

Cited by 7 publications

References 46 publications

Egyptian Hieroglyphs Segmentation with Convolutional Neural Networks

Egyptian Hieroglyphs Segmentation with Convolutional Neural Networks

X-ray Dark-Field Imaging for Improved Contrast in Historical Handwritten Literature

Uncovering eco-friendly design in the ancient bronze goose-and-fish lamp: an unnoticeable gap boosts ventilation

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