DIVA-HisDB: A Precisely Annotated Large Dataset of Challenging Medieval Manuscripts
Foteini Simistira|Mathias Seuret|Nicole Eichenberger|Angelika Garz|Marcus Liwicki|Rolf Ingold
This paper introduces a publicly available historical manuscript database DIVA-HisDB for the evaluation of several Document Image Analysis (DIA) tasks. The consists 150 annotated pages three different medieval manuscripts with challenging layouts. Furthermore, we provide layout analysis ground-truth which has been iterated on, reviewed, and refined by an expert in studies. ground truth can be used training evaluating DIA tasks, such as analysis, text line segmentation, binarization writer identification. Layout results representative baseline technologies are also presented order to help researchers evaluate their methods advance frontiers complex analysis. An optimized state-of-the-art Convolutional Auto-Encoder (CAE) performs around 95% accuracy, demonstrating that this there is much room improvement. Finally, show existing segmentation fail due interlinear marginal elements.