Cross-codex Learning for Reliable Scribe Identification in Medieval Manuscripts

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.

The Applicability of Federated Learning to Official Statistics

This work investigates the potential of Federated Learning (FL) for official statistics and shows how well the performance of FL models can keep up with centralized learning methods. At the same time, its utilization can safeguard the privacy of data holders, thus facilitating access to a broader range of data and ultimately enhancing official statistics. By simulating three different use cases, important insights on the applicability of the technology are gained. The use cases are based on a medical insurance data set, a fine dust pollution data set and a mobile radio coverage data set - all of which are from domains close to official statistics. We provide a detailed analysis of the results, including a comparison of centralized and FL algorithm performances for each simulation. In all three use cases, we were able to train models via FL which reach a performance very close to the centralized model benchmarks. Our key observations and their implications for transferring the simulations into practice are summarized. We arrive at the conclusion that FL has the potential to emerge as a pivotal technology in future use cases of official statistics.