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The digital preservation of Chinese calligraphy, a significant intangible cultural heritage, is impeded by the limitations of traditional generative methods in achieving precise style control and structural fidelity. This paper addresses these shortcomings, which are particularly prevalent in models like CalliGAN, by proposing a novel Transformer-enhanced Generative Adversarial Network (GAN) for calligraphic characters style transfer and generation. Our approach, named Calliformer, tackles three key challenges: (1) We replace RNN-based component encoders, which struggle with capturing global character structures, with a Structure-Aware Transformer encoder. This module models the spatial relationships between components more effectively via a dynamic structural attention bias mechanism. (2) To overcome the limited stylistic diversity of one-hot encodings, we introduce a simple yet effective style encoder that leverages a pre-trained convolutional neural network to extract rich, continuous style embeddings directly from reference images. (3) We introduce CCTS-2025, a publicly available calligraphy dataset annotated with explicit Chinese character structural relationships, to facilitate research in this domain. Experimental results demonstrate that our method achieves a 33.8% reduction in Mean Squared Error (MSE) (from 19.49 to 12.91) and an increase of 0.0965 in Structural Similarity Index (SSIM) compared to the state-of-the-art baseline. Furthermore, a human evaluation study reveals that 91.2% of the generated calligraphy is recognized as authentic and stylistically superior by human participants. This research offers a new paradigm for cultural heritage digitization, with significant application potential in calligraphy education and artifact restoration.

Documenting and preserving cultural heritage assets is increasingly important, with threats from natural disasters, conflicts, climate change, and neglect, and some sites are both contested and physically difficult to access or document, posing the issue of “challenging heritage”. A range of innovative digital methods have emerged, offering practical, low-cost, efficient techniques for the 3D documentation of threatened heritage, including smart phone-based mobile light detection and ranging (LiDAR) and photogrammetry. Such techniques offer quick, accessible, and cost-effective alternatives to terrestrial laser scanners, albeit with reduced accuracy and detail, offering practical solutions in cases with restricted funding, limited time for access, complex architectural geometries, or the unavailability of high-end equipment on site. This paper presents a real-world case study integrating iPhone LiDAR with aerial photogrammetry for the rapid documentation of Sourp Magar Monastery, a Medieval site located in a forested slopes of the Kyrenia Range, Cyprus. Due to its poor state of preservation and years of abandonment, as well as its remote nature and location, the monastery is considered a “challenging heritage” monument. In the context of a recent international restoration initiative, a preliminary digital survey was undertaken to both document the current condition of Sourp Magar and contribute to a better understanding of its construction history. This paper outlines the workflow integrating the use of smartphone LiDAR and aerial photogrammetry, evaluates its efficacy in challenging heritage sites, and discusses its potential implications for rapid, low-cost documentation. Finally, the present paper aims to show the multifaceted benefit of easy-to-use, low-cost technologies in the preliminary study of sites and monuments.

The demand for digitized cultural heritage has increased over the last few decades; digitization is indeed considered a powerful means to preserve and enhance cultural heritage and make it accessible to the wider public for education and research purposes. However, digitizing cultural heritage is a complex process, requiring specific competencies, skills, software, technologies, and, therefore, human and financial resources. Cultural organizations are therefore rethinking their traditional business models, often deciding to outsource their digitization projects or implementing PPPs—public–private partnerships. This is particularly evident in developing countries, where it is also challenging to find the required resources locally and international cooperation is sometimes necessary. However, international PPPs for the digitization of cultural heritage present several unsustainable aspects. There is therefore a need for a deeper understanding of which practices should be implemented to sustainably manage these collaborations. This paper aims to investigate this topic using a case study analysis of two international PPPs for the digitization of cultural heritage in Brazil. The analysis highlights relevant managerial choices that led these projects to positively impact local social, economic, and environmental development, thus bringing early insights to advance the academic and practice discussions on the above-mentioned topic.

The potential of digital technologies in safeguarding and preserving valuable assets have been established over time, predominantly in preserving our increasingly threatened heritage. Technological advancements in digitization and preservation aspects in the form of more sophisticated digitization gadgets have resulted in the practical implications of many digital preservation initiatives. The article discusses the concept of cultural heritage and need of digital preservation for managing and distributing cultural heritage collections through cooperative endeavors. It highlights the importance of incorporating technological advancements for managing effective and long-term cultural preservation projects. It points out various challenges concerning digital preservation initiatives for cultural heritage including financial, technical, policy guidelines, legal aspects and metadata concerns. The article further discusses some important digital preservation projects for managing Cultural Heritage and lists detailed features under each initiative for addressing various challenges.

Die Kulturgutdigitalisierung gehört seit Jahren zum Kerngeschäft der Landesbibliotheken wie der BLB. Dabei lag der Fokus lange Zeit auf der Contentdigitalisierung von unikalem Material. Hinzu kamen regional interessante Bestände wie Adressbücher oder Landtagsprotokolle. Allerdings stellt sich zunehmend die Frage, ob die Aufgabe der Bibliotheken mit der reinen Imageproduktion schon erledigt ist. Am Beispiel der BLB sollen Zukunftsperspektiven aufgezeigt werden, die über die Contentproduktion hinausgehen. Dazu gehört neben der Anreicherung der Digitalisate durch Normdaten, Volltexte und Editionen die Einbindung in überregionale Portale, die Zusammenarbeit mit der Forschung bei ausgewählten Projekten, die Unterstützung kleinerer Einrichtungen und das Engagement in Citizen-Science-Projekten.

Due to the UN and EU’s strong interest in digitizing cultural heritage, the application of 3D scanning technology is gaining importance, even in the case of under-explored areas, such as the 3D scanning of historical clothes. This article discovers and compares methodologies of 3D scanning of historical clothes presented in the literature in order to determine if a new methodology is needed. PRISMA protocol was used to browse scientific sources in an organized way. We posed the following research question: How have 3D scanners been used to digitize historical clothes? The very limited number of works identified, despite our thorough search, allows us to conclude that this topic is very new, and a lot of research can be conducted in the future. We analyzed the methodologies proposed by other authors, taking into account factors such as what was scanned, what was the purpose of scanning, what hardware and software was used, how detailed the description was, etc. It was revealed that other authors explored the topic insufficiently and no complex and coherent methodology of 3D digitization of historical clothes is present. Generally, the field of 3D scanning of historical clothing remains, at this point, very small and fragmented. This work is one of steps to change it.

Amid evolving consumer demands, product design increasingly emphasizes the deeper needs for emotional resonance and cultural identity. Taking Liao–Jin dynasty silk as a case study, this study explores a digital regeneration pathway for traditional cultural colors, evolving from “form–color restoration” to “emotional awakening.” The study focuses on transforming the emotional imagery—such as “mighty” and “dignified”—embedded in the colors of Liao–Jin silk into perceptible, customizable color experiences for modern consumers. To achieve this, an emotional color customization system was constructed through the integration of Interactive Genetic Algorithms (IGA) with the PAD emotional model. Within this system, cultural emotional semantics (e.g., “Powerful,” “Victory”) were quantified as target anchor points in PAD space. The matching degree between color schemes and target emotions is calculated based on user feedback, and is utilized as a fitness function to drive evolution. An experiment was conducted with 48 volunteer evaluators using Liao–Jin silk. Results demonstrated that, compared to traditional IGA, this method achieved significant improvements in emotional matching accuracy: average fitness increased by 34.00%, maximum fitness rose by 10.76%, and the spiritual essence of Liao–Jin culture was more effectively translated into color schemes that evoke positive user emotions. This research offers an innovative solution for cultural heritage digitization, advancing from “form–color restoration” to “emotional and spiritual regeneration.” It also provides a viable approach for intelligent emotional design in fields such as apparel design, cultural creativity, and digital cultural heritage preservation.

This study presents a framework integrating artificial intelligence, augmented reality, and neurophysiological assessment for preserving and digitizing Chinese Keju cultural heritage. Through the implementation of deep learning algorithms, including convolutional neural network with long short-term memory, Transformer with optical character recognition, YOLO-v8 with bidirectional encoder representations from Transformers, and GPT-4V with augmented reality, the authors achieved progressive accuracy improvements from 0.70 to 0.98 across 100 training epochs for cultural artifact recognition tasks. The 32-channel electroencephalogram experiments (n = 25) comparing physical and virtual reality product appreciation revealed distinct neural activation patterns, with physical interaction eliciting improved multisensory integration through enhanced parietal alpha (8–13 Hz) and sensorimotor cortex activation, while virtual reality demonstrated dominant occipital gamma activity, indicating visual processing emphasis. The augmented reality processing pipeline achieved 93.4% throughput with 85.9-ms latency, enabling real-time cultural heritage experiences.

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.

A sustainable smart tourism ecosystem relies on building digital networks that link tourists to destinations. This study explores the potential of web and immersive technologies, specifically the Virtual Romania (VRRO) platform, in enhancing sustainable tourism by redirecting tourist traffic to lesser-known destinations and boosting user engagement through interactive experiences. Our research examines how virtual tourism platforms (VTPs), which include web-based and immersive technologies, support sustainable tourism, complement physical visits, influence user engagement, and foster community building through social features and user-generated content (UGC). An empirical analysis of the VRRO platform reveals high user engagement levels, attributed to its intuitive design and interactive features, regardless of the users’ technological familiarity. Our findings also highlight the necessity for ongoing enhancements to maintain user satisfaction. In conclusion, VRRO demonstrates how accessible and innovative technologies in tourism can modernize travel experiences and contribute to the evolution of the broader tourism ecosystem by supporting sustainable practices and fostering community engagement.