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AI Text-To-Image Procedure for the Visualization of Figurative and Literary Tòpoi

  • Virginia Miele,
  • Marco Saccucci,
  • Assunta Pelliccio

摘要

The intricate bond between text and images has been a subject of contemplation for centuries. Text-To-Image (TTI) technology is a significant advancement, using neural networks to generate images from specific descriptions. In its early stages, this research focuses on developing a method to intercept sentences and relationships to aid the formation of neural networks. Text-to-image (TTI) networks are valuable research tools, efficiently translating written language into visual representations. These neural networks can analyse and compare textual and visual data, making them suitable for this task. Despite their complex structure, neural networks learn rules and activities automatically through learning rather than deductive reasoning, making them innovative tools for analysing the relationship between text and image. The study aims to investigate the potential of AI in transforming written texts into visual representations through practical training. It delves into seventeenth-century writings and modern texts from the 20th century, encompassing diverse linguistic complexities in the Italian language. Converting historical texts into images is a complex process requiring a deep understanding of linguistics. A key aspect involves modulating language to ensure effective communication with AI, which facilitates the generation of optimal image reconstructions. Thus, a robust understanding of the linguistic nuances of the original texts is critical. Future research aims to decode the variables contributing to the most congruous definition of the text-image relationship. The neural network will undergo training in various aspects of visual language, including balance, configuration, shape, and development, to enhance its ability to interpret and translate the “architecture of the text”. This research centres on studying lexical semantics to interpret sentences and understand relationships within them for use in neural networks. It aims to create visual representations of the written descriptions found in different writings.