Machine Learning Suite for IIIF Resources

Many ML pipelines that are publicly available (e.g. on GitHub) use Jupyter notebooks tot rain, test and apply their models. Jupyter notebooks are also very common in DH, in particular to document the generation, manipulation or analysis of datasets. In addition, Jupyter notebooks are used to fulfill teaching aspects. As an example, the NewsEye project with a focus on European newspapers published a notebook collection with an in-depth- analysis of their corpus (e.g. text classification or text similarity) dedicated for the use at university courses.

The main goal of this project is to combine the different technologies – IIIF, ML, Jupyter notebooks – in order to support researchers in generating new knowledge from the digitized cultural assets. Bringing together these technologies has many benefits: (1) it provides an easy, standardized and reusable way to integrate IIIF materials into ML applications in general and (2) it publishes these ML pipelines as Jupyter notebooks. They will be (3) well documented and can therefore (4) be used as a boilerplate for new projects and (5) can easily be applied by other institutions that support IIIF for their data. In addition to raw source code the project also aims to use interactive widget components in the notebooks in order to make the software suite easy to use for users with less previous knowledge in computer sciences.

Machine learning applications are and will be an essential part of data analysis in the scientific context. Regardless of whether small data collections or big data are processed, it is necessary to train users to use machine learning modules. This way, it is possible to understand complex and multidimensional problems and thus create new insights into collections. The project encourages applying these innovative methods to already digitized data.