Study of semi-synthetic plastic objects of historic interest using non-invasive total reflectance FT-IR

A significant proportion of modern and contemporary artifacts and objects of historical interest, are composed of materials in the form of synthetic, semi-synthetic, and natural polymers. Each class of polymer and corresponding composite plastics are subject to different degradation processes. This means that conservators and curators of 20th century collections are faced with varied, nontrivial preservation issues. An unresolved problem is the identification of early plastics based on semi-synthetic polymers such as cellulose nitrate, cellulose acetate, and casein formaldehyde, which were often used to imitate the more valuable natural materials such as ivory, tortoiseshell, ebony, and bone. This exemplifies the need for non-invasive methods specifically tailored for identification of plastic materials in collections, so as to provide conservators with a means of materials classification to support preventive conservation strategies and interventive treatments. The present work is aimed at evaluating the effectiveness of non-invasive Total Reflectance (TR) FT-IR spectroscopy, coupled with a custom reference spectral TR FT-IR library, for the identification of materials comprising a series of unknown objects. A set of ten heritage objects made from early semi-synthetic materials was used as a training test set to validate the proposed methodological approach. The FT-IR data acquired on the test objects were pre-processed and finally classified using commercial software tools used for the automatic classification of spectra in FT-IR spectroscopy. The procedure was successfully applied to several cases, although residual uncertainties remained in a few examples. The results obtained are critically analyzed and discussed in the perspective of proposing a robust method for in-field prescreening of the chemical composition of plastic artistic and historical objects

Assessment of multispectral and hyperspectral imaging systems for digitisation of a Russian icon

In a study of multispectral and hyperspectral reflectance imaging, a Round Robin Test assessed the performance of different systems for the spectral digitisation of artworks. A Russian icon, mass-produced in Moscow in 1899, was digitised by ten institutions around Europe. The image quality was assessed by observers, and the reflectance spectra at selected points were reconstructed to characterise the icon’s colourants and to obtain a quantitative estimate of accuracy. The differing spatial resolutions of the systems affected their ability to resolve fine details in the printed pattern. There was a surprisingly wide variation in the quality of imagery, caused by unwanted reflections from both glossy painted and metallic gold areas of the icon’s surface. Specular reflection also degraded the accuracy of the reconstructed reflectance spectrum in some places, indicating the importance of control over the illumination geometry. Some devices that gave excellent results for matte colour charts proved to have poor performance for this demanding test object. There is a need for adoption of standards for digitising cultural heritage objects to achieve greater consistency of system performance and image quality.This article arose out of a Short-Term Scientific Mission (STSM) conducted by Tatiana Vitorino when visiting University College London during a 2-week period in late October 2015. The research was carried out under the auspices of the European COST Action TD1201 Colour and Space in Cultural Heritage (COSCH). The project website is at http://www.cosch.info. Under the COST rules, TV received funding for travel and accommodation expenses, and all coauthors were able to claim travel expenses to attend the subsequent COSCH project meeting. No other funding was received from COSCH for labour or equipment and all work was done on a voluntary pro bono basis.info:eu-repo/semantics/publishedVersio

A Deep Learning Approach to Ancient Egyptian Hieroglyphs Classification

Nowadays, advances in Artificial Intelligence (AI), especially in machine and deep learning, present new opportunities to build tools that support the work of specialists in areas apparently far from the information technology field. One example of such areas is that of ancient Egyptian hieroglyphic writing. In this study, we explore the ability of different convolutional neural networks (CNNs) to classify pictures of ancient Egyptian hieroglyphs coming from two different datasets of images. Three well-known CNN architectures (ResNet-50, Inception-v3 and Xception) were taken into consideration and trained on the available images. The paradigm of transfer learning was tested as well. In addition, modifying the architecture of one of the previous networks, we developed a specifically dedicated CNN, named Glyphnet, tailoring its complexity to our classification task. Performance comparison tests were carried out and Glyphnet showed the best performances with respect to the other CNNs. In conclusion, this work shows how the ancient Egyptian hieroglyphs identification task can be supported by the deep learning paradigm, laying the foundation for information tools supporting automatic documents recognition, classification and, most importantly, the language translation task