Abstract
The analysis of chain line patterns in historical prints can provide valuable information about the origin of the paper. For this task, we propose a method to automatically detect chain lines in transmitted light images of prints from the 16th century. As motifs and writing on the paper partially occlude the paper structure, we utilize a convolutional neural network in combination with further postprocessing steps to segment and parametrize the chain lines. We compare the number of parametrized lines, as well as the distances between them, with reference lines and values. Our proposed method is an effective method showing a low error of less than 1 mm in comparison to the manually measured chain line distances.
M. Biendl and A. Sindel—Contributed equally to this paper.
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Biendl, M., Sindel, A., Klinke, T., Maier, A., Christlein, V. (2021). Automatic Chain Line Segmentation in Historical Prints. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12663. Springer, Cham. https://doi.org/10.1007/978-3-030-68796-0_47
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DOI: https://doi.org/10.1007/978-3-030-68796-0_47
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