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