  Historic scribe identification is a substantial task for obtaining
information about the past. Uniform script styles, such as the Carolingian
minuscule, make it a difficult task for classification to focus on meaningful
features. Therefore, we demonstrate in this paper the importance of cross-codex
training data for CNN based text-independent off-line scribe identification, to
overcome codex dependent overfitting. We report three main findings: First, we
found that preprocessing with masked grayscale images instead of RGB images
clearly increased the F1-score of the classification results. Second, we
trained different neural networks on our complex data, validating time and
accuracy differences in order to define the most reliable network architecture.
With AlexNet, the network with the best trade-off between F1-score and time, we
achieved for individual classes F1-scores of up to 0,96 on line level and up to
1.0 on page level in classification. Third, we could replicate the finding that
the CNN output can be further improved by implementing a reject option, giving
more stable results. We present the results on our large scale open source
dataset -- the Codex Claustroneoburgensis database (CCl-DB) -- containing a
significant number of writings from different scribes in several codices. We
demonstrate for the first time on a dataset with such a variety of codices that
paleographic decisions can be reproduced automatically and precisely with CNNs.
This gives manifold new and fast possibilities for paleographers to gain
insights into unlabeled material, but also to develop further hypotheses.
