International Journal on Science and Technology

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Precision Reimagined: The AI-Driven Revolution in Colour Management for Paper Printing

Author(s) Mr. Amit Sharma
Country India
Abstract Colour management in print production has traditionally relied on static ICC profiles, manual calibration, and operator heuristics—approaches that struggle to meet the demands of today’s high-speed, short-run, and substrate-diverse printing environments. This chapter explores how Artificial Intelligence (AI) and Machine Learning (ML) are transforming colour management from a deterministic process into an adaptive, data-driven system. Focusing on paper-based printing, it reviews key AI capabilities—such as supervised learning, reinforcement learning, and neural networks—and their application in dynamic calibration, real-time gamut mapping, defect detection, proof simulation, and ink optimization.
Through practical examples and empirical studies, the chapter illustrates how AI enhances print fidelity, reduces waste, and automates quality control. It also examines barriers to adoption, including data quality, integration with legacy systems, and operator retraining. Finally, the chapter projects future developments such as federated learning, hybrid CMS-AI models, and sustainable ink usage optimization. This shift toward intelligent, self-correcting, and environmentally conscious printing systems positions AI not just as a technical enhancer, but as a strategic driver of innovation and efficiency in print colour management.
Keywords Artificial Intelligence, Colour Management, Machine Learning, Print Quality, Gamut Mapping, Ink Optimization, Neural Networks, Predictive Calibration, Paper-Based Printing, Printing Industry 4.0
Field Engineering
Published In Volume 16, Issue 3, July-September 2025
Published On 2025-07-15
DOI https://doi.org/10.71097/IJSAT.v16.i3.7002
Short DOI https://doi.org/g9s9vs
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