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In this contribution it is coined and presented a new concept: "Precision Archaeology'" (PA). PA is a concept based on observing, measuring and responding to inter and intra-field variability in land. PA aims at defining a decision support system for archaeology management for optimizing returns on inputs while preserving resources.

The aim of our research is to artificially generate fake historical manuscripts using GAN. At this stage, these experiments pursue two objectives: evaluating feasibility of generating realistic fake manuscripts under certain conditions of layout, script, or date, and creating artificial data to augment HTR training. Examples are taken from Classical Armenian and Old French.

Artificial Intelligence (AI) technologies such as Deep Learning and Transfer Learning are rapidly advancing text and image classification in many disciplines (e.g., Lucas et al. 2022, Huang et al. 2022, Kumar et al. 2020). With the progressing digitisation of cultural heritage, AI increasingly finds applications in arts and humanities disciplines as well (Cetinic et al. 2019, Saleh / Elgammal 2016). It is evident that AI technologies open new possibilities for processing and analysing large, heterogeneous historical data corpora in a semi-automated way (Im et al. 2022). But do they have the potential to “overturn intellectual legacies” and to revolutionise the field? In the project Ottoman Nature in Travelogues (ONiT 2023) we strive to leverage the application of AI technologies to historical research by developing an interdisciplinary methodological framework for the semi-automated, AI-driven analysis of text–image relations. Our object of study is a large multilingual corpus of travelogues printed 1501–1850 (Rörden et al. 2020) that contains representations of Ottoman “nature” (i.e., flora, fauna, landscapes) both in text and image. This short presentation focuses on our first results achieved for improving image classification of historical prints (i.e., mostly woodcuts, engravings, and etchings). We present our approach to robustly identify and classify key image types in our corpus and discuss challenges and lessons learned during this process. &nbsp

Traditionally, two approaches have developed in dialectology that focus on researching lexical variation: on the one hand, dialect dictionaries, whose task is to document dialect vocabulary; on the other hand, dialect atlases, whose focus is on linguistic-geographical variation in dialect vocabulary. The Wörterbuch der bairischen Mundarten in Österreich (WBÖ), a long-term project of the Austrian Academy of Sciences (ÖAW), is an undertaking of the first type. Until 2015, the first five volumes (A–Ezzes) were published as printed works; since 2018, the Lexikalisches Informationssystem Österreich/Lexical Information System Austria (LIÖ) has served as the publication platform for the articles starting with the letter F. LIÖ is a cooperation project between the Austrian Centre for Digital Humanities and Cultural Heritage (ACDH-CH) of the ÖAW and the FWF Special Research Programme German in Austria (DiÖ) of the University of Vienna. At the moment, the information system only contains content related to the WBÖ project itself. As a lexically oriented platform, however, its content is to be expanded in the coming years, i.e. lexical material from other corpora is also to be made accessible via it. A first expansion of LIÖ will take place in October 2022 with the pilot study DWA Austria, a cooperation project between the Research Center Deutscher Sprachatlas (DSA) of the Philipps University of Marburg and the Department of Linguistics of the ACDH-CH. Within the framework of this cooperation, the entire Austrian surveys of the Deutscher Wortatlas/German Word Atlas (DWA) are to be digitally processed for the first time. Last but not least, the project will serve to expand the paradigm for researching lexical variation described above to include a dialect-geographical component. In this collaboration, the Marburg team will provide the high-resolution scans of the DWA surveys. The team in Vienna is building a model for automatic transliteration on this basis with the help of the Transkribus software, which in turn can be used by the DSA team for the German DWA sheets. The surveys for the DWA were conducted indirectly between 1939 and 1942 and are still among the most comprehensive surveys of the 20th century. Questionnaires were sent to a total of about 50,000 places, 3,700 of which are in the territory of the Republic of Austria. Since previous automatic text recognition methods (i.e. OCR) have only provided insufficient results, the questionnaires, which were mostly handwritten, had to be laboriously transliterated manually. The recent use of artificial intelligence (AI) has been showing promising results for several years. With the help of the Transkribus platform, the Austrian DWA questionnaires are now being captured and made usable as part of a pilot study. Unlike conventional OCR products, Transkribus uses artificial intelligence (AI) to convert the written content of digital records into searchable text. The scans of the DWA sheets were made by the DSA and made available to the ACDH-CH. There, in a first step, they manually transliterate a set of scanned sheets. This step can be supported by already existing models that are, for example, tailored to German Kurrent script. Based on these correct transliterations and the corresponding scans, a model can now be built with the help of Deep Learning that is tailored to the document type. The layout of the text is also taken into account. First models on the minimum amount of training material still showed a rather high error rate (CER Val. 9.4) and have been continuously improved since then. The pilot study DWA Austria shows how data sets that could previously only be used to a limited extent due to time-consuming and costly efforts can now be opened up by AI-supported methods to an extent that would not have been possible with conventional methods. In particular, the example shows how the respective expertise of the individual project partners can bring about a significant increase in efficiency and thus once again illustrates the potential for synergies that result from cooperation. Bibliography: DiÖ = SFB German in Austria. URL: https://www.dioe.at/en/ [2023-05-01] DSA = Forschungszentrum Deutscher Sprachatlas. URL: https://www.uni-marburg.de/en/fb09/dsa [2023-05-01] DWA = Mitzka, Walther & Ludwig Erich Schmitt. 1951 – 1980. Deutscher Wortatlas. Gießen: Schmitz LiÖ = Lexikalisches Informationssystem Österreich. URL: https://lioe.dioe.at/ [2023-05-01] Transkribus. AI powered Handwritten Text Recognition. URL: https://readcoop.eu/transkribus/ [2023-05-01] WBÖ = Österreichischen Akademie der Wissenschaften. 1970 – 2015. Wörterbuch der bairischen Mundarten in Österreich. Wien: Verlag der Österreichischen Akademie der Wissenschaften.

This poster presents the preliminary results from the first author's PhD research project, consisting of the early steps towards customising Computer Vision (CV) to work with features characterising historical photographs from Ireland.

The use of Artificial Intelligence techniques for the automatic recognition of architectural elements from point clouds can provide valuable support to facilitate preservation operations, and support many other activities related to Digital Cultural Heritage domain.

Significance: Hyperspectral imaging (HSI) has emerged as a promising optical technique. Besides optical properties of a sample, other sample physical properties also affect the recorded images. They are significantly affected by the sample curvature and sample surface to camera distance. A correction method to reduce the artifacts is necessary to reliably extract sample properties. Aim: Our aim is to correct hyperspectral images using the three-dimensional (3D) surface data and assess how the correction affects the extracted sample properties. Approach: We propose the combination of HSI and 3D profilometry to correct the images using the Lambert cosine law. The feasibility of the correction method is presented first on hemispherical tissue phantoms and next on human hands before, during, and after the vascular occlusion test (VOT). Results: Seven different phantoms with known optical properties were created and imaged with a hyperspectral system. The correction method worked up to 60 deg inclination angle, whereas for uncorrected images the maximum angles were 20 deg. Imaging hands before, during, and after VOT shows good agreement between the expected and extracted skin physiological parameters. Conclusions: The correction method was successfully applied on the images of tissue phantoms of known optical properties and geometry and VOT. The proposed method could be applied to any reflectance optical imaging technique and should be used whenever the sample parameters need to be extracted from a curved surface sample.

Significance: Polyp size is important for selecting the surveillance interval or treatment policy. Nevertheless, it is challenging to accurately estimate the polyp size during endoscopy. An easy and cost-effective function to assist in polyp size estimation is required. Aim: To propose a virtual scale function for endoscopy and evaluate its performance and expected accuracy. Approach: An adaptive virtual scale behavior was demonstrated. The measurement error of the virtual scale along the distance between the tip of the endoscope and the object plane was evaluated using graph paper. The accuracy of polyp size estimation by an expert endoscopist was compared with the accuracy of the biopsy forceps method using phantom images. Results: The measurement errors of the virtual scale were ≤ 0.7 mm when the distance to the graph paper, which faced the tip of the endoscope, varied from 4 to 30 mm. The accuracy with the virtual scale was significantly higher than that obtained with biopsy forceps (5.3 ± 5.5 % versus 11.9 ± 9.4 % , P

Significance: Screening and early detection of oral potentially malignant lesions (OPMLs) are of great significance in reducing the mortality rates associated with head and neck malignancies. Intra-oral multispectral optical imaging of tissues in conjunction with cloud-based machine learning (CBML) can be used to detect oral precancers at the point-of-care (POC) and guide the clinician to the most malignant site for biopsy. Aim: Develop a bimodal multispectral imaging system (BMIS) combining tissue autofluorescence and diffuse reflectance (DR) for mapping changes in oxygenated hemoglobin (HbO2) absorption in the oral mucosa, quantifying tissue abnormalities, and guiding biopsies. Approach: The hand-held widefield BMIS consisting of LEDs emitting at 405, 545, 575, and 610 nm, 5MPx monochrome camera, and proprietary Windows-based software was developed for image capture, processing, and analytics. The DR image ratio (R610/R545) was compared with pathologic classification to develop a CBML algorithm for real-time assessment of tissue status at the POC. Results: Sensitivity of 97.5% and specificity of 92.5% were achieved for discrimination of OPML from patient normal in 40 sites, whereas 82% sensitivity and 96.6% specificity were obtained for discrimination of abnormal (OPML + SCC) in 89 sites. Site-specific algorithms derived for buccal mucosa (27 sites) showed improved sensitivity and specificity of 96.3% for discrimination of OPML from normal. Conclusions: Assessment of oral cancer risk is possible by mapping of HbO2 absorption in tissues, and the BMIS system developed appears to be suitable for biopsy guidance and early detection of oral cancers.