ChainLineNet: Deep-Learning-Based Segmentation and Parameterization of Chain Lines in Historical Prints

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

Publication Language: English

Publication Type: Journal article

Publication year: 2021

Journal

Journal of Imaging MDPI

Book Volume: 7

Article Number: 120

Journal Issue: 7

URI: https://www.mdpi.com/2313-433X/7/7/120

DOI: 10.3390/jimaging7070120

Open Access Link: https://www.mdpi.com/2313-433X/7/7/120

Abstract

The paper structure of historical prints is sort of a unique fingerprint. Paper with the same origin shows similar chain line distances. As the manual measurement of chain line distances is time consuming, the automatic detection of chain lines is beneficial. We propose an end-to-end trainable deep learning method for segmentation and parameterization of chain lines in transmitted light images of German prints from the 16th Century. We trained a conditional generative adversarial network with a multitask loss for line segmentation and line parameterization. We formulated a fully differentiable pipeline for line coordinates’ estimation that consists of line segmentation, horizontal line alignment, and 2D Fourier filtering of line segments, line region proposals, and differentiable line fitting. We created a dataset of high-resolution transmitted light images of historical prints with manual line coordinate annotations. Our method shows superior qualitative and quantitative chain line detection results with high accuracy and reliability on our historical dataset in comparison to competing methods. Further, we demonstrated that our method achieves a low error of less than 0.7 mm in comparison to manually measured chain line distances.

Authors with CRIS profile

Aline Sindel Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung) Vincent Christlein Lehrstuhl für Informatik 5 (Mustererkennung)

Related research project(s)

Similarity learning for art analysis Critical Catalogue of Luther Portraits (1519-1530) June 1, 2018 - Feb. 28, 2021

Involved external institutions

TH Köln / Technische Hochschule Köln / Fachhochschule Köln DE Germany (DE)

How to cite

APA:

Sindel, A., Klinke, T., Maier, A., & Christlein, V. (2021). ChainLineNet: Deep-Learning-Based Segmentation and Parameterization of Chain Lines in Historical Prints. Journal of Imaging, 7(7). https://doi.org/10.3390/jimaging7070120

MLA:

Sindel, Aline, et al. "ChainLineNet: Deep-Learning-Based Segmentation and Parameterization of Chain Lines in Historical Prints." Journal of Imaging 7.7 (2021).

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