Università degli studi di Modena e Reggio Emilia

The linguistic and social impact of multiculturalism can no longer be neglected in any sector, creating the urgent need of creating systems and procedures for managing and sharing cultural heritages in both supranational and multi-literate contexts. In order to achieve this goal, text sensing appears to be one of the most crucial research areas. The long-term objective of the DigitalMaktaba project, born from interdisciplinary collaboration between computer scientists, historians, librarians, engineers and linguists, is to establish procedures for the creation, management and cataloguing of archival heritage in non-Latin alphabets. In this paper, we discuss the currently ongoing design of an innovative workflow and tool in the area of text sensing, for the automatic extraction of knowledge and cataloguing of documents written in non-Latin languages (Arabic, Persian and Azerbaijani). The current prototype leverages different OCR, text processing and information extraction techniques in order to provide both a highly accurate extracted text and rich metadata content (including automatically identified cataloguing metadata), overcoming typical limitations of current state of the art approaches. The initial tests provide promising results. The paper includes a discussion of future steps (e.g., AI-based techniques further leveraging the extracted data/metadata and making the system learn from user feedback) and of the many foreseen advantages of this research, both from a technical and a broader cultural-preservation and sharing point of view.

Managing and sharing cultural heritages also in supranational and multi-literate contexts is a very hot research topic. In this paper we discuss the research we are conducting in the DigitalMaktaba project, presenting the first steps for designing an innovative workflow and tool for the automatic extraction of knowledge from documents written in multiple non-Latin languages (Arabic, Persian and Azerbaijani languages). The tool leverages different OCR, text processing techniques and linguistic corpora in order to provide both a highly accurate extracted text and a rich metadata content, overcoming typical limitations of current state-of-the-art systems; this will enable in the near future the development of an automatic cataloguer which we hope will ultimately help in better preserving and conserving culture in such a demanding scenario.