For the scope of this thesis, we limit the context to segmenting advertisements from scanned pages of newspapers and magazines. This poses an interesting use-case for, for instance, advertising researchers. Associated colleagues at the University of Mannheim (UniMA) have already manually created a labeled set of 9000 segmented pages of the US magazine "The Economist", ranging from the 1840s to today. We expect a thesis student to develop an interactive labeling system in order to support the extension of this segmentation traing data-set to many more pages. Interactive labeling hereby strives to combine automatic steps (e.g. the trained model) with incremental user input. The work-packages entail:

• analyzing the state-of-the-art of such segmentation tools
• exchange with the researchers at UniMA that created the training data-set regarding requirements and system evaluation
• development of an interactive labeling system as part of a design science research process
  • train a ML classifier based on the existing training data
  • (potentially) include more training data from free data-sets
  • develop an interactive labeling tool that integrates the ML classifier with manual segmentation
  • include novel interaction paradigms with the existing ML classifier into the tool (manually reviewing those instances in which the model was uncertain, retraining the model based on new user input, ...)
• writing a thesis document according to research group requirements & participation in our thesis colloquium

Design science research is a well established methodology in the information systems field, which deals with the scientific view on artifacts, such as the labeling system that should be developed during this thesis. Hereby so called design knowledge can be derived from the development process and the finished artifact.