The paper investigates how artificial intelligence can be used to visualize and analyze the collective imaginary of a place, focusing on the representation of Sicily through textual descriptions. By applying text-to-image AI models to literary excerpts, the research aims to make explicit the intangible and often stereotypical mental images associated with the Sicilian landscape. The methodology combines textual analysis of selected novels with iterative image generation, examining recurring visual patterns and the influence of lexical structures on the output. Results show that AI tends to reproduce dominant visual archetypes (e.g., horizon lines, central compositions, recurring elements such as sea, boats, or rural landscapes), while struggling to interpret syntactic relationships and complex semantic nuances. The study highlights both the potential of AI as a tool for exploring cultural perception and its limitations in translating abstract, narrative-based descriptions into coherent visual representations.
The Recognizability of a Place Through Generative Representation of Intangible Qualities
The paper explores the use of AI-based generative image models to represent the intangible qualities of architectural spaces, such as atmosphere, perception, and emotional experience. Through a comparative experimentation involving multiple platforms (Midjourney, Stable Diffusion with ControlNet, Leonardo.Ai, and Veras), the study investigates how text-to-image and image-to-image processes can translate descriptive prompts and visual inputs into evocative representations. The methodology combines architectural survey outputs (point clouds, photographs, sketches, and watercolors) with textual prompts to guide image generation. Results highlight the varying capabilities of different models in balancing formal coherence and expressive interpretation, demonstrating the potential of AI as a complementary tool for communicating non-measurable aspects of space, while also identifying current limitations in geometric accuracy and semantic control.
Between Image and Text: Automatic Image Processing for Character Recognition in Historical Inscriptions
The research addresses the challenges in Optical Character Recognition (OCR) systems when applied to ancient inscriptions and graffiti. These artifacts, serving celebratory or commemorative purposes, often present legibility issues due to erosion and gaps in the text. Our study proposes an automated image processing pipeline supported by 3D data from photogrammetric surveys. The processing phase involves manipulating image parameters and utilizing spatial coordinates and writing system information. The goal is to enhance legibility by extracting images with neutral backgrounds and highlighted characters, resembling printed texts. This processed data aims to improve the performance of pre-trained Artificial Intelligence (AI) models dedicated to OCR. Ultimately, the research seeks to provide a compar-ative study between unprocessed and processed images, validating the significance of the pre-processing phase in enhancing text recognition systems. The proposed automated workflow aims to contribute to the field of computer vision, specifically in the context of preserving and interpreting historical inscriptions.
A Blockchain-Based Solution to Chain (Im)Material Art
This paper investigates the transformation of art in the digital age, focusing on the shift from material artworks to immaterial digital assets and the implications for authorship, authenticity, and value. Building on theoretical frameworks related to reproducibility, simulacra, and digital representation, the study examines the emergence of NFTs as blockchain-based mechanisms designed to restore uniqueness and ownership within infinitely reproducible environments.
Alongside the theoretical analysis, the research proposes and tests a custom blockchain solution based on Ethereum smart contracts to manage and certify ownership of digital artworks. The system associates artworks with unique identifiers and enables transparent tracking of transactions without relying on centralized platforms. The results demonstrate that low-cost, decentralized infrastructures can effectively support authentication and ownership management, while also highlighting the conceptual and economic limitations of current NFT ecosystems.