This paper presents a 2-stage contextual approach to detect character references using deep learning. The results show that while the precision yields competitive results, the recall is still much lower. Possible approaches for this shortcoming might be changing the loss function - currently a false negative and a false positive yield the same penalty - and combining both models. The state of the art model can then be used for words that only appear a single time in the text and the 2-stage approach for words appearing more than once. This could retain the high quality while still generating a consistent labeling by making use of the dependencies between individual appearances of a word.