Visual analysis consists of using computational processes to study large collections of images. This work can take a variety of forms. Some work in this field has focused on using algorithms to study patterns in visual datasets, such as Lev Manovich’s work on the covers of Time Magazine over time. Others such as the Early Modern OCR Project (eMOP) have focused on using image processing techniques to improve OCR transcription accuracy for researchers working printed texts from the early modern period. Additionally, researchers at the University of Nebraska have used image processing methods to identify poetic passages in unstructured image sets.
Methods & Tools
While there are fewer open source tools for image analysis than there are for text analysis, there are some great resources you can use to get started in this field. For an overview of some of the operations you can perform on images, try looking at the scikit-image gallery, which includes code that can help perform many image operations. The links below offer code that can help you accomplish many popular tasks in image processing:
How do I get started?
If you're new to digital humanities and are interested in starting a project, stop by the Franke Family Digital Humanities Laboratory in Sterling Memorial Library during our Office Hours.
We also highly recommend looking at our Project Planning and Design Toolkit to learn about the steps involved in a typical project life cycle. In addition to projects at Yale, please check out projects at other digital humanities centers, including:
- Stanford's Literary Lab
- Northeastern's NULab for Maps, Texts, and Networks
- Maryland's Institute for Tecnology in the Humanities
- DHCommons Projects
In addition to on-campus support, there are also off-campus and online resources that you might try. The following programs all offer opportunities for researchers to learn different digital humanities methods and theoretical approaches:What we offer