RESHAPING THE FUTURE OF PREPRESS WITH ARTIFICIAL INTELLIGENCE
Innovations in artificial intelligence (AI) are shaping the future of almost every industry, and the printing industry is no exception. Applications of AI in the printing industry can be found in prepress optimization, production automation, variable data printing, quality control, predictive maintenance, customer engagement, workflow improvement, and waste reduction. Prepress is the first, crucial and critical stage in the printing process where files are prepared for the actual printing process. It has always been adaptable to changes due to technological requirements and improvements. The introduction of AI in the production phase of prepress has already demonstrated numerous benefits, such as seamless file management, comprehensive preflight checks, advanced PDF editing and automation of repetitive and time-consuming tasks such as file conversion, layout adjustments, trapping and color correction. AI-powered color management systems, which are essential in prepress, are becoming more sophisticated and can predict and compensate for color differences between different substrates and devices. Although the benefits of AI in prepress are obvious, there are still certain challenges and considerations, including the initial investment, data security, complexity of integration and lack of expertise. This paper aims to explore the integration of AI elements in prepress applications and provide a comprehensive overview of the benefits, challenges and future prospects in this area.
Authors
- Boris Dumnić (ORCID: https://orcid.org/0000-0002-1846-1986)
- Aleksandar S. Anđelković (ORCID: https://orcid.org/0000-0003-2736-6793)
- Željko Zeljković (ORCID: https://orcid.org/0000-0002-6688-107X)
- Sandra Dedijer (ORCID: https://orcid.org/0000-0002-1568-5570)
- Velibor Premčevski (ORCID: https://orcid.org/0000-0002-2883-6956)
- Živko Pavlović (ORCID: https://orcid.org/0000-0002-1480-8786)
- Nemanja Kašıkovıć (ORCID: https://orcid.org/0000-0002-4416-8070)
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.24867/future-bme-2024-102
- Citations
- 1
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 2.28
Funders
- Provincial Secretariat for Higher Education and Scientific Research, Autonomous Province of Vojvodina