Show-through cancellation in scans of duplex printed documents

Analytical and mathematical methods for revealing hidden details in ancient manuscripts and paintings: A review

In this work, a critical review of the current nondestructive probing and image analysis approaches is presented, to revealing otherwise invisible or hardly discernible details in manuscripts and paintings relevant to cultural heritage and archaeology. Multispectral imaging, X-ray fluorescence, Laser-Induced Breakdown Spectroscopy, Raman spectroscopy and Thermography are considered, as techniques for acquiring images and spectral image sets; statistical methods for the analysis of these images are then discussed, including blind separation and false colour techniques. Several case studies are presented, with particular attention dedicated to the approaches that appear most promising for future applications. Some of the techniques described herein are likely to replace, in the near future, classical digital photography in the study of ancient manuscripts and paintings.

Scanning double-sided documents without incurring show-through by learning to fuse two complementary images using multilayer perceptron

This paper presents a novel method for scanning duplex-printed documents without incurring the unwanted show-through artifact. The proposed method achieves the goal of eliminating the leaked-out reverse-side content by fusing a white backed scan image with a black backed scan image of the document. The fusion is accomplished using a multilayer perceptron having learned a fusion mapping from manually corrected document images. The main novel contributions of this work include (1) being the first to propose to accomplish the goal of show through free scanning by fusing a white backed scan image with a black backed scan image of the document; (2) proposing a learning approach using a multilayer perceptron to learn the fusion mapping from manually corrected scan images; and (3) proposing to use the pixel value histogram of reverse-side-printed area as well as the pixel value histogram of duplex-printed area to quantitatively indicate show through severity to facilitate objective comparison of the methods in consideration. The experiment results show that the proposed method is remarkably more powerful in eliminating show through than the two state-of-the-art methods in comparison.

User-assisted ink-bleed reduction

This paper presents a novel user-assisted approach to reduce ink-bleed interference found in old manuscripts. The problem is addressed by first having the user provide simple examples of foreground ink, ink-bleed, and the manuscript's background. From this small amount of user-labeled data, likelihoods of each pixel being foreground, ink-bleed, or background are computed and used as the data costs of a dual-layer Markov random field (MRF) that simultaneously labels all pixels in both the front and back sides of the manuscript. This user-assisted approach produces better results than existing algorithms without the need for extensive parameter tuning or prior assumptions about the ink-bleed intensity characteristics. Our overall application framework is discussed along with details of the features used in the data costs, a comparison between K-nearest neighbor and support vector machine for likelihood estimation, the dual-layer MRF formulation with associated inter- and intra-layer costs, and a comparison of our approach against other ink-bleed reduction algorithms.

A variational approach to degraded document enhancement

The goal of this paper is to correct bleed-through in degraded documents using a variational approach. The variational model is adapted using an estimated background according to the availability of the verso side of the document image. Furthermore, for the latter case, a more advanced model based on a global control, the flow field, is introduced. The solution of each resulting model is obtained using wavelet shrinkage or a time-stepping scheme, depending on the complexity and nonlinearity of the models. When both sides of the document are available, the proposed model uses the reverse diffusion process for the enhancement of double-sided document images. The results of experiments with real and synthesized samples are promising. The proposed model, which is robust with respect to noise and complex background, can also be applied to other fields of image processing.

Multichannel blind separation and deconvolution of images for document analysis

In this paper, we apply Bayesian blind source separation (BSS) from noisy convolutive mixtures to jointly separate and restore source images degraded through unknown blur operators, and then linearly mixed. We found that this problem arises in several image processing applications, among which there are some interesting instances of degraded document analysis. In particular, the convolutive mixture model is proposed for describing multiple views of documents affected by the overlapping of two or more text patterns. We consider two different models, the interchannel model, where the data represent multispectral views of a single-sided document, and the intrachannel model, where the data are given by two sets of multispectral views of the recto and verso side of a document page. In both cases, the aim of the analysis is to recover clean maps of the main foreground text, but also the enhancement and extraction of other document features, such as faint or masked patterns. We adopt Bayesian estimation for all the unknowns and describe the typical local correlation within the individual source images through the use of suitable Gibbs priors, accounting also for well-behaved edges in the images. This a priori information is particularly suitable for the kind of objects depicted in the images treated, i.e., homogeneous texts in homogeneous background, and, as such, is capable to stabilize the ill-posed, inverse problem considered. The method is validated through numerical and real experiments that are representative of various real scenarios.

Document ink bleed-through removal with two hidden Markov random fields and a single observation field

We present a new method for blind document bleed-through removal based on separate Markov Random Field (MRF) regularization for the recto and for the verso side, where separate priors are derived from the full graph. The segmentation algorithm is based on Bayesian Maximum a Posteriori (MAP) estimation. The advantages of this separate approach are the adaptation of the prior to the contents creation process (e.g., superimposing two handwritten pages), and the improvement of the estimation of the recto pixels through an estimation of the verso pixels covered by recto pixels; moreover, the formulation as a binary labeling problem with two hidden labels per pixels naturally leads to an efficient optimization method based on the minimum cut/maximum flow in a graph. The proposed method is evaluated on scanned document images from the 18th century, showing an improvement of character recognition results compared to other restoration methods.

Local image registration by adaptive filtering

We propose a new adaptive filtering framework for local image registration, which compensates for the effect of local distortions/displacements without explicitly estimating a distortion/displacement field. To this effect, we formulate local image registration as a two-dimensional (2-D) system identification problem with spatially varying system parameters. We utilize a 2-D adaptive filtering framework to identify the locally varying system parameters, where a new block adaptive filtering scheme is introduced. We discuss the conditions under which the adaptive filter coefficients conform to a local displacement vector at each pixel. Experimental results demonstrate that the proposed 2-D adaptive filtering framework is very successful in modeling and compensation of both local distortions, such as Stirmark attacks, and local motion, such as in the presence of a parallax field. In particular, we show that the proposed method can provide image registration to: a) enable reliable detection of watermarks following a Stirmark attack in nonblind detection scenarios, b) compensate for lens distortions, and c) align multiview images with nonparametric local motion.