Detecting laboratory objects in early modern illustrations

Early modern technical handbooks increasingly draw on and re-use illustrations of laboratory equipment that are investigated using computer vision methods following the Distant Viewing paradigm.

The computer vision subproject investigates alchemical laboratory objects through computer vision methods applied to a corpus of early modern handbooks and technical treatises. These works incorporated increasingly more sophisticated illustrations of laboratory equipment and experimental setups. The project builds on an annotated computer vision dataset (Lang, 2025) and explores the possibilities of computational image analysis in this context. At the same time, it critically examines the limitations of computer vision approaches for early modern materials (Lang, 2026). The primary challenge is not the visual style of the images themselves but the difficulty of annotating and analysing concepts that contemporary algorithms have not been trained to recognise (Lang et al., 2023). This reveals forms of bias that often remain hidden within computational approaches commonly associated with distant viewing in the computational humanities.

References

2026

  1. (Doing) Computational History: On the Role of Data Work in Computational Approaches
    Sarah Lang
    Histories, 2026
    DOI

2025

  1. Fine-Tuning Machine Learning with Historical Data: An Alchemical Object Detection Dataset for Early Modern Scientific Illustrations
    Sarah Lang
    Zeitschrift für digitale Geisteswissenschaften, 2025
    DOI

2023

  1. Toward a Computational Historiography of Alchemy: Challenges and Obstacles of Object Detection for Historical Illustrations of Mining, Metallurgy, and Distillation in 16th–17th Century Print
    Sarah A. Lang, Bernhard Liebl, and Manuel Burghardt
    In Proceedings of the Computational Humanities Research Conference 2023 (CHR 2023), 2023