Machine Learning–Driven Typesetting Inspired by Human Design Practices

Published by Kushtrim Hamzaj
July 10, 2025

The third use case introduces a smart typesetting method that uses machine learning to make intelligent, human-like typographic decisions. Unlike rule-based typesetting, where typesetting decisions rely on hard-coded algorithms, this prototype proposes a system that uses machine learning methods (such as optical character recognition (OCR) and neural networks) to analyze the work of previous type designers and learn from their visual and structural decisions. This approach allows computers to make typographic design decisions based on human logic.

Initialization and Setup

Unlike print, which uses a fixed medium, screens come in a variety of sizes and resolutions, making it difficult to achieve the same level of typographic precision. Historically, typesetting has always presented challenges on digital screens, and current rule-based systems, which rely on predefined if-then conditions, have not fully succeeded in addressing these issues. In his paper ‘The Pursuit of Quality: How Can Automated Typesetting Achieve the Highest Standards of Craft Typography’, Chris Rowley emphasises that, although automated systems aim to replicate the precision of skilled typographers, they often fail in areas requiring aesthetic judgement and contextual understanding.

The full Article is coming soon.

References:

Rowley, C., & Mittelbach, F. (1991). The Pursuit of Quality How can automated type-
setting achieve the highest standards of craft typography? In EP92 – Proceedings
of Electronic Publishing.

AITYDE – Artificial Intelligence in Typography and Design
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