Search Results (63)

Early printed books, particularly incunabula, are invaluable archives of the beginnings of modern educational systems. However, their complex layouts, antique typefaces, and page degradation caused by bleed-through and ink fading pose significant challenges for automatic transcription. In this work, we present a modular pipeline that addresses these problems by combining modern layout analysis and language modeling techniques. The pipeline begins with historical layout-aware text segmentation using Kraken, a neural network-based tool tailored for early typographic structures. Initial optical character recognition (OCR) is then performed with Kraken’s recognition engine, followed by post-correction using a fine-tuned ByT5 transformer model trained on manually aligned line-level data. By learning to map noisy OCR outputs to verified transcriptions, the model substantially improves recognition quality. The pipeline also integrates a preprocessing stage based on our previous work on bleed-through removal using robust statistical filters, including non-local means, Gaussian mixtures, biweight estimation, and Gaussian blur. This step enhances the legibility of degraded pages prior to OCR. The entire solution is open, modular, and scalable, supporting long-term preservation and improved accessibility of cultural heritage materials. Experimental results on 15th-century incunabula show a reduction in the Character Error Rate (CER) from around 38% to around 15% and an increase in the Bilingual Evaluation Understudy (BLEU) score from 22 to 44, confirming the effectiveness of our approach. This work demonstrates the potential of integrating transformer-based correction with layout-aware segmentation to enhance OCR accuracy in digital humanities applications. Full article

Processing ancient text images presents significant challenges due to severe visual degradation, missing glyph structures, and various types of noise caused by aging. These issues are particularly prominent in Chinese historical documents and stone inscriptions, where diverse writing styles, multi-angle capturing, uneven lighting, and low contrast further hinder the performance of traditional OCR techniques. In this paper, we propose a unified neural framework, UniText, for the detection, recognition, and glyph restoration of Chinese characters in images of historical documents and inscriptions. UniText operates at the character level and processes full-page inputs, making it robust to multi-scale, multi-oriented, and noise-corrupted text. The model adopts a multi-task architecture that integrates spatial localization, semantic recognition, and visual restoration through stroke-aware supervision and multi-scale feature aggregation. Experimental results on our curated dataset of ancient Chinese texts demonstrate that UniText achieves a competitive performance in detection and recognition while producing visually faithful restorations under challenging conditions. This work provides a technically scalable and generalizable framework for image-based document analysis, with potential applications in historical document processing, digital archiving, and broader tasks in text image understanding. Full article

The COVID-19 pandemic introduced new challenges for the Galleries, Libraries, Archives, and Museums (GLAM) sector, including drastically reduced visitation, constrained funding, and pressure to increase the amount and richness of digital rather than physical interaction. The authors draw upon explorative projects to examine the potential of serious games, augmented and virtual reality, and community-led design to address GLAM sector challenges raised by COVID-19. The initial findings suggest that while digital heritage projects often suffer from limited interactivity and accessibility, participatory game design and low-cost XR (extended reality) tools can enhance engagement and learning. The article concludes that open-ended, collaborative, and context-aware game frameworks offer a promising path for GLAM institutions to sustain relevance, support digital literacy, and adapt to post-pandemic challenges. Full article

Accurate extraction of table borders in scanned road engineering drawings is crucial for the digital transformation of engineering archives, which is an essential step in the development of intelligent infrastructure systems. However, challenges such as degraded borders, image blur, and character adjoining often hinder the precise delineation of table structures, making automated parsing difficult. Existing solutions, including traditional OCR tools and deep learning methods, struggle to consistently delineate table borders in the presence of these visual distortions and fail to perform well without extensive annotated datasets, which limits their effectiveness in real-world applications. We propose TableBorderNet, a semantic segmentation framework designed for precise border extraction under complex visual conditions. The framework captures structural context by guiding convolutional feature extraction along explicit row and column directions, enabling more accurate delineation of table borders. To ensure topological consistency in complex or degraded inputs, a topology-aware loss function is introduced, which explicitly penalizes structural discontinuities during training. Additionally, a generative self-supervised strategy simulates common degradation patterns, allowing the model to achieve strong performance with minimal reliance on manually annotated data. Experiments demonstrate that the method achieves an Intersection-over-Union of 94.2% and a topological error of 1.07%, outperforming existing approaches. These results underscore its practicality and scalability for accelerating the digitization of engineering drawings in support of data-driven road asset management. Full article

Archives have the potential to contribute to national development by preserving historical records and providing access to information. However, their impact is constrained by ineffective outreach strategies, insufficient institutional investment, and low public visibility. Public programming has been used as a strategic approach to bridge the gap between archival institutions and their user communities through engagement initiatives. Therefore, the objective of this study was to systematically review and analyse the diverse public programming activities undertaken by archival institutions globally and to identify the resultant outcomes of these engagements. To achieve this, the study employed a systematic literature review methodology, examining scholarly publications to synthesise existing evidence on public engagement in archives, thereby providing a comprehensive overview of current practices and their demonstrated impacts. The systematic review was conducted in accordance with PRISMA guidelines, utilising a two-stage selection process involving a search of six databases and four specialised journals. This search yielded 39 publications that met the inclusion criteria. Methodological rigour was evaluated using the CASP checklist. The results from the study indicated that exhibitions, educational programmes, community outreach, and digital initiatives were the most common public programming strategies. These activities enhance public awareness, increase accessibility, and foster community engagement. Despite the availability of various public programming activities, challenges such as inadequate funding, lack of digital infrastructure, and bureaucratic constraints hinder their effectiveness. The need for structured outreach strategies, institutional support, and the integration of emerging technologies to optimise public programming in archives is emphasised. The findings contribute to improving archival accessibility and user engagement in a rapidly evolving digital landscape. Full article

The conservation of experimental building materials that were introduced during the 20th-century currently represents one of the main challenges in building restoration. Fair-faced concrete is especially affected by durability problems and requires careful assessment to implement effective conservation methods, even more so when the building has artistic and expressive value. In addition, the literature in this field is still limited and case studies are very rare. In this paper, the Partisan Ossuary Monument, a brutalist monument at the Certosa of Bologna, was studied and analysed in order to find the most effective restoration techniques, especially for its concretes, which have a particularly expressive texture. The aim was to combine both the preservation of the aesthetics and functional quality of the building with the use of existing technologies in this field. Firstly, archive research was carried out to discover the original building techniques and the materials used. The literature on the Monument was studied to unveil the expressive role given to the concretes’ surface finishing. Then, after an on-site investigation, all the materials used in the Monument and the degradation processes were analysed and mapped out. Significant samples of the Monument were manually collected whilst limiting invasiveness. Then, diagnostic tests were carried out to identify the causes of degradation and to comprehend the nature of certain superficial finishes. Several techniques were used, i.e., X-ray diffraction, optical microscopy, and FT-IR spectrometry. Finally, guidelines were drafted for possible future restoration, merging all the results from the previous phases of this study with compliance with heritage structures’ restoration requirements. Many technologies commonly used for the repair of concrete structures could not be applied to this Monument due to its features. Hence, new solutions were studied and proposed. The results obtained may contribute to an increased awareness of the need to restore 20th-century heritage buildings in order to limit degradation and partial reconstruction. Many concrete heritage buildings of this period suffer from the same problems, and this paper could offer an important starting point for future research. Full article

Since the early 21st century, BIM technology has enhanced building design, construction and management, while continuously evolving to create new specializations. Despite this, its full potential remains untapped. Today, BIM offers diverse applications in construction and related industries, incorporating advanced techniques such as laser scanning and photogrammetry. A specialized approach, HBIM (Heritage Building Information Modeling), enables the digital mapping, documentation, analysis and management of historic architecture. This study focuses on the Koprowski Family Villa in Celestynów, known as “Koprówka”, demolished twenty years ago. Despite its cultural significance, the property disappeared from the village. Using LiDAR survey data, preserved window frames, archival photographs and documents, this engineering study reconstructs “Koprówka” as an HBIM model, integrated into the existing landscape. The resulting 3D model can aid municipal authorities and potential investors in rebuilding “Koprówka”, while emphasizing the importance of cultural heritage in shaping local identity and raising awareness of historical structures’ value. Full article

Since 2011, five of Austria’s 29 known prehistoric pile dwellings have been part of the transnational UNESCO World Heritage Site “Prehistoric Pile Dwellings around the Alps”. These remarkable archaeological sites have been preserved for over 7000 years in lakes and moors. Due to their hidden location underwater or in the soil of bogs, the sites are inaccessible to the public, making it difficult to convey the full scope of this heritage. To address this, the national project “Surfaced—the digital pile dwellings” was launched, aiming to create a virtual bridge connecting the sites, collections, and exhibitions across Austria. It involved digitizing 500 objects, scanned in high resolution, and presenting them as 3D models in an open-access web application. The web application PfahlbauKompass allows users to explore these 3D models, view information about the artefacts and the sites, and create digital collections. It provides access to finds from national museums, regional heritage houses, and private collections. The project offers scientific potential as well as opportunities for virtual exhibitions and educational initiatives. It aims to preserve and visualize an essential part of Austria’s cultural heritage and was designed not only to archive finds but also to raise awareness of the hidden sites among the public. Full article

Background/Objectives: During retroperitoneal surgery, awareness of the anatomic variants of renal arteries (RAs) is essential. We aimed to determine the prevalence of early-branched (short) Ras, the bilateral morphologies of RAs in such cases, and to check for significant correlations regarding gender or side. Short RAs may be regarded as false multiple RAs and should be distinguished from true RAs. Methods: For the study, 185 archived angioCT files were randomly selected and evaluated for <1.5 cm RAs (106 male and 79 female cases). Simple regression and multiple regression tests, alongside ANOVA, were used for the statistical analysis. Results: Short RAs were found in 15/185 cases, 12 males and 3 females (8.1%), with short RAs found on the right side (2.7%), left side (4.86), and bilaterally (one case, 0.54%). The mean length was 9.46 mm. Short RAs were bifurcated in most cases and trifurcated in one case. In four other cases, peculiar RA anatomical patterns were found. They included a right RA origin of the right inferior phrenic artery, variable polar RAs, malrotated and ptotic kidneys, anteriorly dehiscent renal sinuses, and multiple RAs, including five right RAs, with the three inferior ones having precaval courses. Short RAs were not significantly related to gender (p > 0.05). There was a significant correlation between gender and right short RAs (p < 0.05). Conclusions: During renal transplant surgery, distinguishing between true and false multiple RAs is essential. While true multiple RAs may cause surgical discomfort, short RAs may be used as single RAs, but they should be carefully documented before donor nephrectomies. Full article

One of the strategies to address the persistent youth mental health crisis is school-based educational programming. This paper reports on two distinct studies regarding Coming Up for AIR, a school-based mental health education program: (1) program effectiveness, measured as gains in student mental health literacy; and (2) trends in help-seeking behavior before, during, and after the pandemic. A survey on program content was administered to assess program effectiveness. Data collected between 2020 and 2023 from four schools yielded 473 responses. A comparison of pre- and post-scores demonstrated statistically significant gains in program content. Mental health literacy improved across gender and grade level (8th, 9th, and 10th), as well as for students with prior exposure to a mental health curriculum. In the second study, help-seeking behavior was evaluated before, during, and after the pandemic. While other programs measure intention, Coming Up for AIR measures actual behavior as students can ask for help for themselves or a friend. Data did not reflect individual student responses, but rather were aggregated and provided the number of students per presentation who requested help. Archived declaration card data from January 2019 through February 2024 was accessed, representing 28 different schools and 16,289 middle and high school student responses. School-level data were analyzed by grade level (middle school or high school) and date (pre-, intra-, or post-COVID-19). Significant differences in self-referral were found for both grade level and presentation date. Self-referrals were significantly higher post-COVID-19 compared to pre-COVID-19 with middle schoolers increasing 90% and high school students increasing 36%. Analysis of friend referrals showed a significant difference for grade level, but not presentation date. Again, middle school students were more likely to make a referral than high schoolers. The data suggest that the mental health crisis in middle school students persists at an alarming rate. Schools are at the forefront of addressing mental health issues for youth. External educational programming can bring awareness to mental health concerns and promote help-seeking in youth. Full article

In the three decades since a digital archive of satellite-detected night-time lights (NTL) data was created, thousands of scholarly articles have been published using these data. An important change in the last decade saw a significant share of highly cited articles with NTL data now written by economists. The way that economists treat the literature in other disciplines potentially interferes with the diffusion of updated findings on NTL data. Our bibliometric analysis finds that many economics studies using NTL data, especially highly cited ones, ignore studies by the remote sensing scientists who help provide the NTL data. This review considers two implications of the growing distance in the literature between economists using NTL data and remote sensing scientists. First, newer, more accurate and precise NTL data from sources like VIIRS (Visible Infrared Imaging Radiometer Suite) have slower uptake in economics, perhaps due to a lack of awareness. Yet, economists using NTL data increasingly work with spatially disaggregated units, for which the older, coarser, DMSP data are less suited. Second, a misunderstanding of DMSP spatial resolution leads to pixel-level regression studies in economics that are potentially subject to measurement error bias, for which we provide two case studies. Overall, the full value of NTL-based research may not be realized due to these weak connections. Full article

This essay examines relationships between adoptees and the (extended) adoptive family, focusing on the inheritance rights of adopted persons as entry points into levels and cycles of their belonging and un-belonging. The essay contextualizes a case report (or summary reports) on the kind of estrangement in the adoptee world that is fueled by inheritance disputes. It delves into postadoption perceptions and thus into the “unwritten” truths about adoption and its possible fallout. It draws from archival sources, semi-structured interviews (life-story interviewing), and life writing by adoptees, and also from a sequence of real-life exchanges dating back to 2018. All these sources focus on the contested inheritance of children, now older adults, who were adopted from Greece in the 1950s–60s and who became (or should have become) subsequent heirs to the estates of their adoptive parents and/or relatives. The Greek out-of-country adoptions of the postwar and early Cold War era involved more than 4000 children, most of whom were sent to the United States. The various testimonies and sections reflect critically on the continuing trend to infantilize the adopted persons, forever the adopted children, to push their origins back into the past and into geographical distance, to untie the family connections they have forged over the course of half a century. The examples take the reader from the adoptive family’s pre-adoption attempts at disowning the child through the postadoption stage of the end of an adopted lifetime, including cases of the extended adoptive family’s attempts at “de-adopting” the adopted person. This essay includes various sources of life-cycle documentation, among them an extensive case study and online obituaries. It adheres to truth and authenticity by incorporating fairly long original quotations, which, in the case study of the second half especially, assist the reader in comprehending much historical information in a question-and-answer format. This bolder structure offers the advantage of taking the reader step by step through the transactions of a prominent Greek adoption scheme (Rebecca and Maurice Issachar) and also through the various layers of the postadoption mindset and minefield. The material presented here is intended to raise awareness that change can and must still benefit the Greek adoptees today, whose lives may have been permeated by conditionality and nonlinearity. I conclude that, in the cases discussed here, the child’s orphanhood may well be a perpetual state, with the adoptee being orphaned of individuality and of a protective family on more than just one occasion. Full article

Purpose: The objective of this study was to determine the prevalence of exostosis in the Mississippi population. Methods: The patient archives of the UMMC School of Dentistry between January 2018 and May 2021 were examined. Patients with exostosis were included in the study based on the findings from intraoral photographs, full-moth series (FMS), panoramic radiographs, and cone-beam computerized tomography (CBCT) images of the patients. Patients were excluded from the study if they did not have maxillary or mandibular exostosis. In addition, each patient’s age, gender, and ethnic origins, such as Caucasian, African-American, or Asian, were recorded. All data regarding exostosis were evaluated and categorized according to age, gender, and ethnicity. Multiple logistic regression analysis was performed for more detailed statistical analysis. Results: A total of 1242 patients were examined for the presence of maxillary and mandibular tori. In total, 303 patients were diagnosed with maxillary and/or mandibular tori among the Mississippi population, with a prevalence rate of 24.4%. Exostosis was seen more in females (57.4%) compared to males (42.6%). The highest prevalence of exostosis was seen in Caucasians (71.3%), followed by African-Americans (23.8%) and Asians (5%). Conclusions: High prevalence of exostosis was seen in the Mississippi population. Our findings will help clinicians to be more aware of these bony protuberances and, thus, better at diagnosing them. This may specifically help oral surgeons, periodontists, and prosthodontists in pre-prosthodontic surgery and denture fabrication. Full article

The anthrax pathogen Bacillus anthracis can remain dormant as spores in soil for many years. This applies to both natural foci and to sites of anthropogenic activity such as tanneries, abattoirs, or wool factories. The A.Br.075 (A-branch) clade (also known as A.Br.Sterne) is prominent not only because it comprises several outbreak strains but even more so because spore preparations of its namesake, the Sterne strain, are counted among the most utilized anthrax animal vaccines. In this study, we genome-sequenced and analyzed 56 additional B. anthracis isolates of the A.Br.075 clade. Four of these we recently retrieved from soil samples taken from a decades-long abandoned tannery. The other 52 strains originated from our archival collection from the 20th century. Notably, the extended phylogeny of the A.Br.075 clade indicated that many of the newly added chromosomes represent basal members, some of which are among the most basal strains from this lineage. Twelve new strains populate a very deep-branching lineage we have named A.Br.Ortho-Sterne (also known as A.Br.076). A further 11 isolates amend the clade named A.Br.Para-Sterne (A.Br.078). Finally, some of the terminal clusters of the clade named A.Br.Eu-Sterne appear to be replete with (near) identical isolates, possibly a result of widespread use of the Sterne vaccine and of its re-isolation from vaccination-related animal anthrax outbreaks. From the accrued new phylogenetic information, we designed and tested a variety of new SNP-PCR assays for rapid and facile genotyping of unassigned B. anthracis genomes. Lastly, the successful isolation of live B. anthracis from a long-abandoned tannery reemphasizes the need for continued risk awareness of such sites. Full article

The semantic segmentation of bone structures demands pixel-level classification accuracy to create reliable bone models for diagnosis. While Convolutional Neural Networks (CNNs) are commonly used for segmentation, they often struggle with complex shapes due to their focus on texture features and limited ability to incorporate positional information. As orthopedic surgery increasingly requires precise automatic diagnosis, we explored SegFormer, an enhanced Vision Transformer model that better handles spatial awareness in segmentation tasks. However, SegFormer’s effectiveness is typically limited by its need for extensive training data, which is particularly challenging in medical imaging, where obtaining labeled ground truths (GTs) is a costly and resource-intensive process. In this paper, we propose two models and their combination to enable accurate feature extraction from smaller datasets by improving SegFormer. Specifically, these include the data-efficient model, which deepens the hierarchical encoder by adding convolution layers to transformer blocks and increases feature map resolution within transformer blocks, and the FPN-based model, which enhances the decoder through a Feature Pyramid Network (FPN) and attention mechanisms. Testing our model on spine images from the Cancer Imaging Archive and our own hand and wrist dataset, ablation studies confirmed that our modifications outperform the original SegFormer, U-Net, and Mask2Former. These enhancements enable better image feature extraction and more precise object contour detection, which is particularly beneficial for medical imaging applications with limited training data. Full article