Workshop: Applications of Computer Vision in Digital Humanities
A.Y. 2025/2026
Learning objectives
This workshop introduces participants to the foundational concepts and methodologies of computer vision, with a particular focus on applications within the digital humanities. Through theoretical insights and practical tools, it aims to:
-Present key artificial intelligence techniques, especially supervised deep learning models.
-Explore computer vision tasks such as image classification, semantic segmentation, and text recognition.
-Demonstrate how to prepare and process visual datasets for humanities research.
-Foster an understanding of tools like Roboflow, Google Teachable Machine, and Transkribus for real-world applications.
-Present key artificial intelligence techniques, especially supervised deep learning models.
-Explore computer vision tasks such as image classification, semantic segmentation, and text recognition.
-Demonstrate how to prepare and process visual datasets for humanities research.
-Foster an understanding of tools like Roboflow, Google Teachable Machine, and Transkribus for real-world applications.
Expected learning outcomes
By the end of this workshop, students will be able to:
-Understand Core Concepts: Explain the distinctions between AI, machine learning, and deep learning, and describe their relevance to computer vision.
-Apply CV Techniques: Identify and implement basic computer vision tasks, such as image classification, using appropriate tools.
-evelop Annotated Datasets: Create and manage labeled image datasets suitable for training classification models using platforms like Roboflow.
-Experiment with AI Tools: Use Google Teachable Machine for training simple classifiers and interpret the results.
-Process Historical Manuscripts: Apply convolutional neural networks for handwritten text recognition through tools such as Transkribus.
-Critically Engage with Humanities Data: Reflect on the methodological challenges and opportunities of applying CV in digital humanities contexts.
Students will also have access to all materials via the myAriel platform and can consult the instructor during office hours or by email.
-Understand Core Concepts: Explain the distinctions between AI, machine learning, and deep learning, and describe their relevance to computer vision.
-Apply CV Techniques: Identify and implement basic computer vision tasks, such as image classification, using appropriate tools.
-evelop Annotated Datasets: Create and manage labeled image datasets suitable for training classification models using platforms like Roboflow.
-Experiment with AI Tools: Use Google Teachable Machine for training simple classifiers and interpret the results.
-Process Historical Manuscripts: Apply convolutional neural networks for handwritten text recognition through tools such as Transkribus.
-Critically Engage with Humanities Data: Reflect on the methodological challenges and opportunities of applying CV in digital humanities contexts.
Students will also have access to all materials via the myAriel platform and can consult the instructor during office hours or by email.
Lesson period: Second semester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
This 20-hour hands-on laboratory provides a practical and critical introduction to the application of Artificial Intelligence in historical and cultural studies. The course adopts a "no-code" approach, focusing on building conceptual understanding and practical skills through user-friendly tools. Students will journey from the foundational concepts of AI and data literacy to hands-on experience with user-friendly platforms for image classification (Teachable Machine), object detection (Roboflow), and handwritten text recognition (Transkribus). The course is highly interactive and culminates in a student-led final project, where participants will either present a critical analysis of a scholarly article on AI in the humanities or showcase the results of a small-scale applied project.
Prerequisites for admission
No prior knowledge of programming or computer science is required. Familiarity with using computers and basic software applications is sufficient. A strong interest in exploring the intersection of digital technologies and humanities research is essential.
Teaching methods
The course adopts an active learning approach, combining conceptual lectures, critical discussions, and hands-on workshops with digital tools. Active participation is essential to foster a dynamic and collaborative learning environment. Class attendance is mandatory.
Teaching Resources
1) Slides, handouts, and scholarly articles provided by the instructor
2) Books on computer vision to support learning (these are not a prerequisite, just references):
- Foundations of Computer Vision by Antonio Torralba, Phillip Isola, and William T. Freeman (MIT Press, 2024): A comprehensive introduction to core computer vision concepts, suitable for those new to the field. (Online https://mitpress.ublish.com/ebook/foundations-of-computer-vision-1/12791/418)
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (Adaptive Computation and Machine Learning series): A more advanced text focused on deep learning techniques, ideal for students seeking in-depth understanding of the algorithms behind computer vision. (online: https://www.deeplearningbook.org/)
- Biological and Computer Vision by Gabriel Kreiman (Cambridge University Press, 2021): Explores both biological and computational vision, providing useful insights into vision systems and their application in computer vision, accessible for students with a basic understanding of the subject. (online: https://klab.tch.harvard.edu/publications/Books/BiologicalAndComputerVision/TableOfContents.html)
- Artificial Intelligence, A Guide for Thinking Humans by Melanie Mitchell (2019): Provides a clear and accessible overview of AI concepts, challenges, and the history of the field. It is a helpful resource for students seeking to understand the broader context of AI and its implications beyond computer vision (https://melaniemitchell.me/aibook/)
- Pixels & Paintings: Foundations of Computer-assisted Connoisseurship by David G. Stork (2023): A specialized resource connecting computer vision to art and heritage analysis, can be useful for students interested in art and cultural heritage applications.
3) Transkribus: Offers valuable online resources for handwritten text recognition. The platform provides guides and tools for working with historical manuscript images, suitable for students engaged in transcription tasks: https://www.transkribus.org/blog
2) Books on computer vision to support learning (these are not a prerequisite, just references):
- Foundations of Computer Vision by Antonio Torralba, Phillip Isola, and William T. Freeman (MIT Press, 2024): A comprehensive introduction to core computer vision concepts, suitable for those new to the field. (Online https://mitpress.ublish.com/ebook/foundations-of-computer-vision-1/12791/418)
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (Adaptive Computation and Machine Learning series): A more advanced text focused on deep learning techniques, ideal for students seeking in-depth understanding of the algorithms behind computer vision. (online: https://www.deeplearningbook.org/)
- Biological and Computer Vision by Gabriel Kreiman (Cambridge University Press, 2021): Explores both biological and computational vision, providing useful insights into vision systems and their application in computer vision, accessible for students with a basic understanding of the subject. (online: https://klab.tch.harvard.edu/publications/Books/BiologicalAndComputerVision/TableOfContents.html)
- Artificial Intelligence, A Guide for Thinking Humans by Melanie Mitchell (2019): Provides a clear and accessible overview of AI concepts, challenges, and the history of the field. It is a helpful resource for students seeking to understand the broader context of AI and its implications beyond computer vision (https://melaniemitchell.me/aibook/)
- Pixels & Paintings: Foundations of Computer-assisted Connoisseurship by David G. Stork (2023): A specialized resource connecting computer vision to art and heritage analysis, can be useful for students interested in art and cultural heritage applications.
3) Transkribus: Offers valuable online resources for handwritten text recognition. The platform provides guides and tools for working with historical manuscript images, suitable for students engaged in transcription tasks: https://www.transkribus.org/blog
Assessment methods and Criteria
The overall assessment of the theoretical and applied skills acquired will be based on a final oral examination focusing on learning materials provided (50%), on the preparation of individual or collective work (30%), and on the active participation during classes (20%).
The final oral exam consists of an oral discussion, designed to assess the students' understanding of the main concepts introduced during the course, their ability to explain with clarity and to link the different topics and issues addressed, their capacity to analyse case studies with appropriate theoretical tools and critical awareness.
The final oral exam consists of an oral discussion, designed to assess the students' understanding of the main concepts introduced during the course, their ability to explain with clarity and to link the different topics and issues addressed, their capacity to analyse case studies with appropriate theoretical tools and critical awareness.
Professor(s)