Bleed-through removal in degraded documents

Abstract

This paper presents a linear-based restoration method for bleed-through degraded document images and uses a Bayesian approach for bleed-through reduction. A variation of iterated conditional modes (ICM) optimisation is used whereby samples are drawn for the clean image estimates, whilst the remaining variables are estimated via the mode of their conditional probabilities. The proposed method is tested on various samples of scanned manuscript images with different degrees of degradation, and results visually compared with a recent user-assisted restoration method.

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

“…The recto sides are depicted in the left and the verso sides in the right, respectively. From top to bottom, we see the original degraded images, the results from the methods in [5,10,18], and [6], respectively, and finally, the results from the method proposed herein. It is apparent that the results obtained with our method are fully qualitatively comparable to the best ones, produced by the method in [6].…”

“…3a, d. From top to bottom: originals, results from the method in [5], results from the method in [18], results from the method in [10], results from the method in [6], and results from our method image. The values of these indices for the two experiments above are summarized in Table 1.…”

“…The values of these indices for the two experiments above are summarized in Table 1. The authors of [6] quantified the performance of their method and those of the methods in [5,18] and [10] by binarizing the reconstructions with Gatos binarization [26] and then computing the mean quality indices of Eq. (10) for the entire dataset.…”

“…When a linear mixing model is assumed, fast algorithms are available, such as those based on Independent Component Analysis (ICA) and data decorrelation [7,8]. Within this approach, compensations for the apparent nonlinear-ity and/or non-stationarity of the physical phenomenon have used regularization techniques using MRF [9,10] or penalized nonnegative matrix factorization (NMF) [11]. In [12], a nonlinear convolutional mixing model is derived by approximating the physical model of the show-through in modern scanners, and adaptive linear filters are used to estimate the unknown model parameters.…”

A Ground Truth Bleed-Through Document Image Database

“…Alternatively, performance may be evaluated without any ground truth by quantifying how the restoration affects a secondary step, such as the performance of an Optical Character Recognition (OCR) system on the document image, [17], [16]. A further issue with quantitative evaluations for performance comparison is that results of different methods are often in different formats, such as binary images [1], pseudo-binary images where the background is uniform with varying foreground intensities [10], [8], or a textured background medium with varying foreground and background intensities [17], [11]. We propose that a fair quantitative comparison between methods can only be achieved if they are converted to the same format then compared to a ground truth that is also of the same format, and the simplest way of achieving this is to binarise all the results and compare them to a binary ground truth.…”