Computer Vision for Digital Humanists (School and Videos)
This summer school introduces computer vision and machine learning methods for image-based research in the Digital Humanities.
Instructor: Sean Winslow, Sarah Lang et al.
Course Overview
The school introduces the foundations of computer vision, deep learning, and digital image analysis from a Digital Humanities perspective. Participants learn how images are represented computationally, how supervised machine learning workflows are constructed, and how computer vision methods can be applied to humanities data.
Practical topics include the creation of training data, evaluation and interpretation of machine learning models, image management with Tropy, dataset preparation in Python, and the development of computer vision workflows from simple prototype models to more advanced applications. The school was designed specifically for Digital Humanities researchers and students without extensive prior training in computer science.
This summer school was co-organised by Sarah Lang, Sean Winslow, and colleagues as part of the CLARIAH-AT project Computer Vision for Digital Humanists. The in-person event was used to develop materials for an openly available video course for self-study.