Revolution or Evolution? AI-Driven Image Classification of Historical Prints

Artificial Intelligence (AI) technologies such as Deep Learning and Transfer Learning are rapidly advancing text and image classification in many disciplines (e.g., Lucas et al. 2022, Huang et al. 2022, Kumar et al. 2020). With the progressing digitisation of cultural heritage, AI increasingly finds applications in arts and humanities disciplines as well (Cetinic et al. 2019, Saleh / Elgammal 2016). It is evident that AI technologies open new possibilities for processing and analysing large, heterogeneous historical data corpora in a semi-automated way (Im et al. 2022). But do they have the potential to “overturn intellectual legacies” and to revolutionise the field? In the project Ottoman Nature in Travelogues (ONiT 2023) we strive to leverage the application of AI technologies to historical research by developing an interdisciplinary methodological framework for the semi-automated, AI-driven analysis of text–image relations. Our object of study is a large multilingual corpus of travelogues printed 1501–1850 (Rörden et al. 2020) that contains representations of Ottoman “nature” (i.e., flora, fauna, landscapes) both in text and image. This short presentation focuses on our first results achieved for improving image classification of historical prints (i.e., mostly woodcuts, engravings, and etchings). We present our approach to robustly identify and classify key image types in our corpus and discuss challenges and lessons learned during this process. &nbsp