Automatic Chain Line Segmentation in Historical Prints

Biendl M, Sindel A, Klinke T, Maier A, Christlein V (2021)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2021

Publisher: Springer, Cham

Series: Lecture Notes in Computer Science

Pages Range: 657-665

Conference Proceedings Title: Pattern Recognition. ICPR International Workshops and Challenges

Event location: Online (Milan, Italy)

ISBN: 978-3-030-68796-0

DOI: 10.1007/978-3-030-68796-0_47


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.

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Biendl, M., Sindel, A., Klinke, T., Maier, A., & Christlein, V. (2021). Automatic Chain Line Segmentation in Historical Prints. In Pattern Recognition. ICPR International Workshops and Challenges (pp. 657-665). Online (Milan, Italy): Springer, Cham.


Biendl, Meike, et al. "Automatic Chain Line Segmentation in Historical Prints." Proceedings of the International Workshop on Fine Art Pattern Extraction and Recognition (FAPER) at International Conference on Pattern Recognition (ICPR), Online (Milan, Italy) Springer, Cham, 2021. 657-665.

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