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Computer Vision for the Humanities: An Introduction to AI Deep Learning for Image Classification (past event)

Daniel van StrienDaniel van Strien is a Digital Curator at the British Library working on the Living with Machines project. The Living with Machines project seeks to use computational methods to work with digitised collections ‘at scale’, this has included work with digitised newspapers, maps and census records. Daniel is particularly interested in making computational methods, in particular machine learning, more accessible and useful to domain experts.  

How might we better understand, explore and investigate the diverse range of images we encounter in the humanities?

A two day in-person workshop convened by the Centre for Digital Inquiry. Led by Daniel van Strien, Digital Curator, British Library.

Over the last 10 years, the field of computer vision, which seeks to gain a high-level understanding of images using computational techniques, has seen rapid innovation. For example, computer vision models are able to locate and identify people, animals and thousands of objects on images with high levels of accuracy.

Computer vision promises the same innovation for images that the combination of Optical Character Recognition (OCR) or Natural language processing (NLP) techniques caused for texts. They open up a part of the digital archive for large-scale analysis, which, until now, has been left uncovered: the millions of images in digitized books, newspapers, periodicals, and historical documents. Recent applications of these methods for humanities-related questions include projects that extract images from historical newspapers, analyse Visual Style in Two Network Era Sitcoms’, and work with large digitised map collections.

By the end of the workshop you should be in a position to identify opportunities to use computer vision in your discipline, know how to identify candidate datasets and have an appreciation of what’s involved in a project involving computer vision. The hands-on aspects of the workshop will give you exposure to key considerations when working with deep learning and you’ll be able to tweak provided examples to conduct your own prototype experiments simply in a lab environment.

This workshop is made possible through grant funding from EPSRC: EP/W032201/1 Expanding, supporting, and training the Sulis tier 2 user community.

Places are limited to UK research-focussed roles at Higher Education Institutions, Galleries, Libraries, Archives and Museums only. Reservations are made on a first-come, first-served basis. 50% of the places are reserved for Warwick researchers. Registration will close on 16th March 2022.


Monday 21st - Tuesday 22nd March 2022, 10:00 - 16:30 (1hr lunch at 12:30 and 30-minute afternoon break at 15:00)

Registration is free. If you can no longer make the workshop, please make sure you let us know at

Research Exchange, LibraryLink opens in a new window. Given the intensive and hands-on nature of this workshop, it is being offered in person only.
Learning objectives

The first day of the workshop will:

  • Provide an introduction to deep learning-based computer vision methods for humanities research.
  • Give an overview of the steps involved in training a deep learning model.
  • Discuss some of the specific considerations around using deep learning/computer vision for humanities research.
  • Help you decide whether deep learning might be a useful tool for you.

The second day of the workshop will:

  • Go into more depth about key considerations around the construction of datasets for training a computer vision model.
  • Provide an opportunity to participate in a hands-on project focused on building a computer vision model focused on a humanities research question.
Prerequisites/ preparation/ requirements

Suggested pre-requisites: You are welcome to join without the below experience but having some familiarity with these topics will be helpful. If you don’t have this experience you will still be able to participate in the hands-on sections of the workshop.

  • Some familiarity with Python or another programming language will be helpful. Specifically, it would be beneficial to understand how to use variables, indexing, and to have some familiarity with using methods from external libraries. You are still welcome to join without this existing experience.
  • Some basic familiarity with using Jupyter Notebooks will be an advantage i.e. knowing how to run the code included in a Jupyter notebook. If you are unfamiliar with notebooks you may find the introduction to Jupyter Notebooks Programming Historian lesson a helpful resource prior to the session.

Technical requirements: participants will need a laptop or personal computer with internet access and a modern browser (Firefox or Chrome preferred). It might be possible to follow materials with a tablet but I won’t be able to troubleshoot issues with this kind of setup.

Registration form

This form is closed and is no longer accepting any submissions.

If you want to be put on a waiting list, please contact Dr Godwin Yeboah (

Thank you for your time.