ICDAR2017 Competition on Layout Analysis for Challenging Medieval Manuscripts

Abstract

This paper reports on the ICDAR2017 Competition on Layout Analysis for Challenging Medieval Manuscripts (HisDoc-Layout-Comp) and provides further details and discussions. In this competition we introduce a new challenging dataset and state-of-the-art benchmark results for pixel-labelling and text line segmentation. The DIVA-HisDB comprises medieval manuscripts with complex layout in contrast to previous datasets, where rectangular text blocks and only a few decorative elements exist. In particular, the images of this competition contain many interlinear and marginal glosses as well as texts in various sizes and decorated letters. This makes the distinction of the four target labels (text, comment, decoration, and background) more difficult. In addition, to reflect the needs of scholars in the humanities, we request multi-labeling of certain regions (decorated text as text and decoration). Furthermore, we measure not just the accuracy, but the Intersection over Union (IU) of pixel sets, which better reflects the real performance. Indeed, in our results we observe that the accuracy appears to be rather high, but the IU reveals, that there is still room for improvement. For the task of line segmentation, the recognition results are rather low (overall error higher than 5%). Noteworthy, a combination of the best layout analysis method with an adapted seam-carving based method achieves better results than the best contestant.