Automatic Chain Line Segmentation in Historical Prints

Biendl, Meike; Sindel, Aline; Klinke, Thomas; Maier, Andreas; Christlein, Vincent

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

Cited by 2 publications

(5 citation statements)

References 5 publications

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“…In our previous work [6], we trained a convolutional neural network (CNN) to automatically segment the chain lines in artworks. Therefore, we employed the UNet [7] as the network architecture and proposed two postprocessing steps by employing either random sample consensus (RANSAC) [8] or the Hough transform to locate and parameterize complete lines in the binarized segmentation results.…”

Section: Segmentation and Detection Of Chain Linesmentioning

confidence: 99%

“…In this section, we measure the performance of our ChainLineNet compared to competing methods. We retrained the UNet architecture (F = 16) of our previous work [6], which was implemented in TensorFlow, for our renewed historical print dataset for 30 epochs using a learning rate of η = 0.0001 and a batch size of 5. During inference, the UNet was executed patchwise, and two postprocessing methods were applied to the reassembled segmentation output [6], which we refer to as PatchUNet-RANSAC and PatchUNet-Hough.…”

Section: Comparison To the State-of-the-artmentioning

confidence: 99%

“…We retrained the UNet architecture (F = 16) of our previous work [6], which was implemented in TensorFlow, for our renewed historical print dataset for 30 epochs using a learning rate of η = 0.0001 and a batch size of 5. During inference, the UNet was executed patchwise, and two postprocessing methods were applied to the reassembled segmentation output [6], which we refer to as PatchUNet-RANSAC and PatchUNet-Hough. Secondly, we trained the deep Hough transform line prior method [12] for our line detection task, which we abbreviate as PatchDeepHough.…”

Section: Comparison To the State-of-the-artmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Segmentation and Detection Of Chain Linesmentioning

confidence: 99%

Section: Comparison To the State-of-the-artmentioning

confidence: 99%

Section: Comparison To the State-of-the-artmentioning

confidence: 99%

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

et al. 2021

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“…They use canny edge detection to additionally extract the watermark. Most recently, both Biendl et al [ 12 ] and Sindel et al [ 13 ] develop deep learning approaches for chain line segmentation and parametrisation from transmitted light images. While [ 12 ] follow a supervised approach to train a UNet for chain line segmentation, [ 13 ] train a conditional generative adversarial network to predict a segmentation mask.…”

Section: Introductionmentioning

confidence: 99%

“…Most recently, both Biendl et al [ 12 ] and Sindel et al [ 13 ] develop deep learning approaches for chain line segmentation and parametrisation from transmitted light images. While [ 12 ] follow a supervised approach to train a UNet for chain line segmentation, [ 13 ] train a conditional generative adversarial network to predict a segmentation mask. However, both approaches rely on supervised training which in turn requires manually annotated ground truth segmentation masks and is therefore costly.…”

Section: Introductionmentioning

confidence: 99%

Extracting chain lines and laid lines from digital images of medieval paper using spectral total variation decomposition

Grossmann,

Schönlieb,

Da Rold

2023

Herit Sci

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Medieval paper, a handmade product, is made with a mould which leaves an indelible imprint on the sheet of paper. This imprint includes chain lines, laid lines and watermarks which are often visible on the sheet. Extracting these features allows the identification of the paper stock and gives information about the chronology, localisation and movement of manuscripts and people. Most computational work for feature extraction of paper analysis has so far focused on radiography or transmitted light images. While these imaging methods provide clear visualisation of the features of interest, they are expensive and time consuming in their acquisition and not feasible for smaller institutions. However, reflected light images of medieval paper manuscripts are abundant and possibly cheaper in their acquisition. In this paper, we propose algorithms to detect and extract the laid and chain lines from reflected light images. We tackle the main drawback of reflected light images, that is, the low contrast attenuation of chain and laid lines and intensity jumps due to noise and degradation, by employing the spectral total variation decomposition and develop methods for subsequent chain and laid line extraction. Our results clearly demonstrate the feasibility of using reflected light images in paper analysis. This work enables feature extraction for paper manuscripts that have otherwise not been analysed due to a lack of appropriate images. We also open the door for paper stock identification at scale.

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Automatic Chain Line Segmentation in Historical Prints

Cited by 2 publications

References 5 publications

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

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

Extracting chain lines and laid lines from digital images of medieval paper using spectral total variation decomposition

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