A Ground Truth Bleed-Through Document Image Database

Rowley-Brooke, Róisín; Pitié, François; Kokaram, Anil

doi:10.1007/978-3-642-33290-6_21

Cited by 28 publications

(17 citation statements)

References 15 publications

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“…We compared the proposed method with other state-of-the-art methods including [7,16]. For evaluation, we used images from the well known database of ancient documents presented in [63,64]. This database contains 25 pairs of recto-verso images of ancient manuscripts affected by bleed-through, along with ground truth images.…”

Section: Resultsmentioning

confidence: 99%

Non-Local Sparse Image Inpainting for Document Bleed-Through Removal

et al. 2018

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Bleed-through is a frequent, pervasive degradation in ancient manuscripts, which is caused by ink seeped from the opposite side of the sheet. Bleed-through, appearing as an extra interfering text, hinders document readability and makes it difficult to decipher the information contents. Digital image restoration techniques have been successfully employed to remove or significantly reduce this distortion. This paper proposes a two-step restoration method for documents affected by bleed-through, exploiting information from the recto and verso images. First, the bleed-through pixels are identified, based on a non-stationary, linear model of the two texts overlapped in the recto-verso pair. In the second step, a dictionary learning-based sparse image inpainting technique, with non-local patch grouping, is used to reconstruct the bleed-through-contaminated image information. An overcomplete sparse dictionary is learned from the bleed-through-free image patches, which is then used to estimate a befitting fill-in for the identified bleed-through pixels. The non-local patch similarity is employed in the sparse reconstruction of each patch, to enforce the local similarity. Thanks to the intrinsic image sparsity and non-local patch similarity, the natural texture of the background is well reproduced in the bleed-through areas, and even a possible overestimation of the bleed through pixels is effectively corrected, so that the original appearance of the document is preserved. We evaluate the performance of the proposed method on the images of a popular database of ancient documents, and the results validate the performance of the proposed method compared to the state of the art.

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Section: Resultsmentioning

confidence: 99%

Non-Local Sparse Image Inpainting for Document Bleed-Through Removal

et al. 2018

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“…Although these ground truth images are synthetic, i.e., created manually, they can be useful for a quantitative analysis of the results. This database is diffusely described in [24].…”

Section: Discussion Of the Experimental Resultsmentioning

confidence: 99%

“…This means that the Fig. 6 Application of the proposed method to a real recto-verso pair from dataset [23,24]: a original degraded recto, b original degraded verso, c recto restored with the method in [6], d verso restored with the method in [6], e recto restored with the proposed method, and f verso restored with the proposed method Fig. 7 Application of the proposed method to an RGB real-fake palimpsest: a green channel, d blue channel, b and e results of ICA applied to the density maps, c overwriting extracted with our method, and f underwriting extracted with our method binarization algorithm underestimates the threshold between foreground and background.…”

Section: Discussion Of the Experimental Resultsmentioning

confidence: 99%

A non-stationary density model to separate overlapped texts in degraded documents

Tonazzini

Savino

Salerno

2014

SIViP

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We address the problem of the removal of a text superimposed to a more important one, in a document image, considering the two instances of canceling back-tofront interferences from recto and verso images of archival documents and of recovering the erased text in palimpsests from multispectral images. Both problems are approached through a model where the ideal images of the two texts are considered as individual source patterns, mixed through some parametric operator. To cope with occlusions, ink saturation, and space variability of the mixing operator, a data model for this problem should be nonlinear and space variant. Here, we show that if a pointwise non-stationarity is allowed, a linear model can compensate for the lack of a suitable nonlinearity and for other modeling errors.

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“…The proposed method was tested on the database of 25 manuscript recto-verso image pairs with manually created binary foreground text images, presented in [13]. The results are evaluated first subjectively via a visual comparison, and then objectively, via a numerical comparison, against three recent non-blind bleed-through removal methods: (i) The dual-layer MRF approach with user trained likelihood proposed by Huang et al [8], referred to as 'H' in the results, (ii) The double sided wavelet model of Moghaddam and Cheriet [10], referred to as 'M', and (iii) The linear model based approach of Rowley-Brooke and Kokaram [12], referred to as 'R'.…”

Section: Resultsmentioning

confidence: 99%

“…In Section 3.2 the two stage classification process is presented, and in Section 3.3 the details of the restoration stage are described. Visual and numerical comparisons between the proposed method and three recent non-blind removal methods, using the database and methodology proposed in [13] are made in Section 4, and finally the conclusions are presented in Section 5.…”

Section: Introductionmentioning

confidence: 99%

A Non-parametric Framework for Document Bleed-through Removal

Rowley-Brooke

Pitié

Kokaram

2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

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This paper presents recent work on a new framework for non-blind document bleed-through removal. The framework includes image preprocessing to remove local intensity variations, pixel region classification based on a segmentation of the joint recto-verso intensity histogram and connected component analysis on the subsequent image labelling. Finally restoration of the degraded regions is performed using exemplar-based image inpainting. The proposed method is evaluated visually and numerically on a freely available database of 25 scanned manuscript image pairs with ground truth, and is shown to outperform recent non-blind bleed-through removal techniques.

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A Ground Truth Bleed-Through Document Image Database

Cited by 28 publications

References 15 publications

Non-Local Sparse Image Inpainting for Document Bleed-Through Removal

Non-Local Sparse Image Inpainting for Document Bleed-Through Removal

A non-stationary density model to separate overlapped texts in degraded documents

A Non-parametric Framework for Document Bleed-through Removal

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