Automated text analysis as named entity recognition (NER) heavily relies on large amounts of high-quality training data. Transfer learning approaches aim to overcome the problem of lacking domain-specific training data. In this paper, we investigate different transfer learning approaches to recognize unknown domain-specific entities, including the influence on varying training data size. The experiments are based on the revised German SmartData Corpus, and a baseline model, trained on this corpus.