Degraded Historical Document Binarization: A Review on Issues, Challenges, Techniques, and Future Directions
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paper_title: Fast handwriting recognition for indexing historical documents

paper_content:

Handwriting recognition (HR) has been successfully used in several applications such as postal address interpretation [S. Srihari et al., (1997)], bank check reading [S. Impedovo et al., (1997)], and forms reading [S. Madhvanath et al., (1995)]. These applications are all characterized by small or fixed lexicons afforded by contextual knowledge. Machine recognition of handwriting in historical documents presents two primary challenges: (i) large lexicons (over 10000 words) leading to low recognition accuracy (less than 50%) and (ii) a need for high speed HR given the millions of handwritten manuscripts in digital library repositories and that the speed is usually inversely proportional to lexicon size. We address the issue of speed when dealing with large lexicons. We present several techniques to improve the processing speed for a gain of up to 7 times in matching time and describe a method whereby the large lexicon is divided into smaller sets and processed in parallel. With 4 processors 18 times speedup for the matching phase is achieved.
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paper_title: Difficult and urgent open problems in document image analysis for libraries

paper_content:

Open problems in document image analysis research and development relevant to digital libraries (DLs) are briefly described and their difficulty and urgency estimated. They are grouped according to the stage of processing, in the construction of DLs, where they first apply: image capture, early processing, content extraction and recognition, structure analysis, retrieval, & presentation; and in personal and interactive DLs.
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paper_title: Document image analysis for World War II personal records

paper_content:

Complete collections of invaluable documents of unique historical and political significance are decaying and at the same time they are virtually inaccessible, necessitating the invention of robust and efficient methods for their conversion into a searchable electronic form. We present the issues encountered and problems addressed in the MEMORIAL project, whose goal is the establishment of a digital document workbench enabling the creation of distributed virtual archives based on documents existing in libraries, archives, museums, memorials, and public record offices. Successful approaches are described in the context of the chosen data class: a variety of typewritten documents containing personal information relating to the presence of individuals in World War II Nazi concentration camps.
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paper_title: A general system for the retrieval of document images from digital libraries

paper_content:

Large collections of scanned documents (books and journals) are now available in digital libraries. The most common method for retrieving relevant information from these collections is image browsing, but this approach is not feasible for books with more than a few dozen pages. The recognition of printed text can be made on the images by OCR systems, and in this case a retrieval by textual content can be performed. However, the results heavily depend on the quality of original documents. More sophisticated navigation can be performed when an electronic table of contents of the book is available with links to the corresponding pages. An opposite approach relies on the reduction of the amount of symbolic information to be extracted at the storage time. This approach is taken into account by document image retrieval systems. We describe a system that we developed in order to retrieve information from digitized books and journals belonging to digital libraries. The main feature of the system is the ability of combining two principal retrieval strategies in several ways. The first strategy allows an user to find pages with a layout similar to a query page. The second strategy is used in order to retrieve words in the collection matching a user-defined query, without performing OCR. The combination of these basic strategies allows users to retrieve meaningful pages with a low effort during the indexing phase. We describe the basic tools used in the system (layout analysis, layout retrieval, word retrieval) and the integration of these tools for answering complex queries. The experimental results are made on 1287 pages and show the effectiveness of the integrated retrieval.
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paper_title: Pixel-wise binarization of musical documents with convolutional neural networks

paper_content:

Binarization is an important process in document analysis systems. Yet, it is quite difficult to devise a binarization method that perform successfully over a wide range of documents, especially in the case of digitized old musical manuscripts and scores with irregular lighting and source degradation. Our approach to binarization of musical documents is based on training a Convolutional Neural Network that classifies each pixel of the image as either background or foreground. Our results demonstrate that the approach is competitive with other state-of-the-art algorithms. It also illustrates the advantage of being able to adapt to any type of score by simply modifying the training set.
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paper_title: Parsing floor plan images

paper_content:

This paper introduces a method for analyzing floor plan images using wall segmentation, object detection, and optical character recognition. We introduce a challenging new real-estate floor plan dataset, R-FP, evaluate different wall segmentation methods, and propose fully convolutional networks (FCN) for this task. We explore architectures with different pixel-stride values and more compact ones with skipped pooling layers. An FCN-2s with a 2-pixel stride layer achieves state-of-the-art performance, obtaining a mean Intersection-over-Union score of 89.9% on R-FP, and 94.4% on the public CVC-FP data set. Using OCR and object detection, we estimate room sizes. Finally, we show applications in automatic 3D model building and interactive furniture fitting.
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paper_title: A database for degraded Arabic historical manuscripts

paper_content:

Data base is important as it help conducting research more effectively. This paper proposes a database for degraded Arabic historical manuscripts dating to the Islamic and ancient Arabic eras. The documents in the database exhibit different types of degradation such as smears, uneven illumination, contrast variation, blur, deteriorated paper, bleed-through, faded ink or faint characters, and thin or weak text. There are three documents for each type of degradation, and there is a ground truth image of each document for use in later performance measurements and verification. This database aims to facilitate a range of image processing experiments on Arabic degraded documents
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paper_title: ICFHR2016 Competition on the Analysis of Handwritten Text in Images of Balinese Palm Leaf Manuscripts

paper_content:

This paper presents the results of the Competition on the Analysis of Handwritten Text in Images of Balinese Palm Leaf Manuscripts that was organized in the context of the 15th International Conference on Frontiers in Handwriting Recognition (ICFHR-2016). This competition provides a suitable challenge for testing and evaluation of robustness for some methods, image features and descriptors which were already proposed for handwritten text analysis of document image. In this competition, three different challenges in document analysis of palm leaf manuscript images are proposed: Challenge 1: Binarization of Palm Leaf Manuscript Images, Challenge 2: Query-by-Example Word Spotting on Palm Leaf Manuscript Images, and Challenge 3: Isolated Character Recognition of Balinese Script in Palm Leaf Manuscript Images. The first handwritten Balinese palm leaf manuscript dataset, the AMADI_LontarSet, is used for performance evaluation. This paper describes the competition details including the dataset creation and the ground truth construction, the evaluation measures used, a short description of each participant as well as the performance of the all submitted methods.
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paper_title: A QUANTITATIVE COMPARISON OF IMAGE RESTORATION METHODS FOR CONFOCAL MICROSCOPY

paper_content:

In this paper, we compare the performance of three iterative methods for image restoration: the Richardson–Lucy algorithm, the iterative constrained Tikhonov–Miller algorithm (ICTM) and the Carrington algorithm. Both the ICTM and the Carrington algorithm are based on an additive Gaussian noise model, but differ in the way they incorporate the non-negativity constraint. Under low light-level conditions, this additive (Gaussian) noise model is a poor description of the actual photon-limited image recording, compared with that of a Poisson process. The Richardson–Lucy algorithm is a maximum likelihood estimator for the intensity of a Poisson process. We have studied various methods for determining the regularization parameter of the ICTM and the Carrington algorithm and propose a (Gaussian) prefiltering to reduce the noise sensitivity of the Richardson–Lucy algorithm. The results of these algorithms are compared on spheres convolved with a point spread function and distorted by Poisson noise. Our simulations show that the Richardson–Lucy algorithm, with Gaussian prefiltering, produces the best result in most of the tests. Finally, we show an example of how restoration methods can improve quantitative analysis: the total amount of fluorescence inside a closed object is measured in the vicinity of another object before and after restoration.
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paper_title: A database for degraded Arabic historical manuscripts

paper_content:

Data base is important as it help conducting research more effectively. This paper proposes a database for degraded Arabic historical manuscripts dating to the Islamic and ancient Arabic eras. The documents in the database exhibit different types of degradation such as smears, uneven illumination, contrast variation, blur, deteriorated paper, bleed-through, faded ink or faint characters, and thin or weak text. There are three documents for each type of degradation, and there is a ground truth image of each document for use in later performance measurements and verification. This database aims to facilitate a range of image processing experiments on Arabic degraded documents
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paper_title: Illumination and Contrast Correction Strategy using Bilateral Filtering and Binarization Comparison

paper_content:

Illumination normalization and contrast variation on images are one of the most challenging tasks in the image processing field. Normally, the degrade contrast images are caused by pose, occlusion, illumination, and luminosity. In this paper, a new contrast and luminosity correction technique is developed based on bilateral filtering and superimpose techniques. Background pixels was used in order to estimate the normalized background using their local mean and standard deviation. An experiment has been conducted on few badly illuminated images and document images which involve illumination and contrast problem. The results were evaluated based on Signal Noise Ratio (SNR) and Misclassification Error (ME). The performance of the proposed method based on SNR and ME was very encouraging. The results also show that the proposed method is more effective in normalizing the illumination and contrast compared to other illumination techniques such as homomorphic filtering, high pass filter and double mean filtering (DMV).
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paper_title: Estimating image blur in the wavelet domain

paper_content:

In this paper, a wavelet based method is proposed to estimate the blur in an image using information contained in the image itself. We look at the sharpness of the sharpest edges in the blurred image, which contain information about the blurring. Specifically, a smoothness measure, the Lipschitz exponent, is computed for these sharpest edges. Its value is related to the variance of a gaussian point spread function. This value is only dependent on the blur in the image and not on the image contents. This allows us to estimate the variance of the blur directly from the image itself. With minor modifications, the method can be extended to other types of blur that are described by one parameter (airy disks, out-of-focus blur, …).
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paper_title: Image Enhancement Technique on Contrast Variation: A Comprehensive Review

paper_content:

Image enhancement is very important, especially for the analysis and diagnosis of detailed information. Most of the studies conducted in image enhancement focus on contrast normalization. Generally, contrast determines how information in images can be perceived easily, how various details in the image can be easy distinguished or how objects of interest can be located. In this paper, a comprehensive review of image enhancement based on spatial domain (Histogram Equalization (HE) and Homomorphic Filtering) and frequency domain (Discrete Wavelet Transform (DWT)) is presented. The improvement and modification of methods were explained systematically. The objective of this work was to study the advantages and drawbacks for each of the method based on a comparison of the results performance. Besides that, this research focuses on various types of applications, emphasizing the importance of contrast enhancement for the improvement of its performance, especially in terms of accuracy and sensitivity. Previous studies were reviewed and critically compared to gain a better understanding of image enhancement. New ideas for further research improvement in image enhancement were proposed.
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paper_title: Illumination and Contrast Correction Strategy using Bilateral Filtering and Binarization Comparison

paper_content:

Illumination normalization and contrast variation on images are one of the most challenging tasks in the image processing field. Normally, the degrade contrast images are caused by pose, occlusion, illumination, and luminosity. In this paper, a new contrast and luminosity correction technique is developed based on bilateral filtering and superimpose techniques. Background pixels was used in order to estimate the normalized background using their local mean and standard deviation. An experiment has been conducted on few badly illuminated images and document images which involve illumination and contrast problem. The results were evaluated based on Signal Noise Ratio (SNR) and Misclassification Error (ME). The performance of the proposed method based on SNR and ME was very encouraging. The results also show that the proposed method is more effective in normalizing the illumination and contrast compared to other illumination techniques such as homomorphic filtering, high pass filter and double mean filtering (DMV).
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paper_title: Separating text and background in degraded document images - a comparison of global thresholding techniques for multi-stage thresholding

paper_content:

Before any processing of the textual content of a document image can be performed the text must be separated from the background of the image. Several thresholding algorithms have previously been proposed and are widely used in document processing. None have been shown effective at thresholding difficult documents where the background and foreground are non-uniform. In this paper we investigate the use of three global thresholding algorithms (Otsu's, Kapur's entropy and Solihin's quadratic integral ratio (QIR)) as the first stage in a multi-stage thresholding algorithm for use in degraded document images. It is concluded that Otsu's and Kapur's algorithms do not work well for difficult documents as they tend to over-threshold the image, thus losing much of the useful information. The QIR algorithm is more accurate in separating the foreground and background in these images, leaving a range of undecided, fuzzy, pixels for later processing in a subsequent stage.
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paper_title: ISauvola: Improved Sauvola’s Algorithm for Document Image Binarization

paper_content:

Binarization of historical documents is difficult and is still an open area of research. In this paper, a new binarization technique for document images is presented. The proposed technique is based on the most commonly used binarization method: Sauvola’s, which performs relatively well on classical documents, however, three main defects remain: the window parameter of Sauvola’s formula does not fit automatically to the image content, is not robust to low contrasts, and not invariant with respect to contrast inversion. Thus on documents such as magazines, the content may not be retrieved correctly. In this paper we use the image contrast that is defined by the local image minimum and maximum in combination with the computed Sauvola’s binarization step to guarantee good quality binarization for both low and correctly contrasted objects inside a single document, without adjusting manually the user-defined parameters to the document content.
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paper_title: Image Enhancement Technique on Contrast Variation: A Comprehensive Review

paper_content:

Image enhancement is very important, especially for the analysis and diagnosis of detailed information. Most of the studies conducted in image enhancement focus on contrast normalization. Generally, contrast determines how information in images can be perceived easily, how various details in the image can be easy distinguished or how objects of interest can be located. In this paper, a comprehensive review of image enhancement based on spatial domain (Histogram Equalization (HE) and Homomorphic Filtering) and frequency domain (Discrete Wavelet Transform (DWT)) is presented. The improvement and modification of methods were explained systematically. The objective of this work was to study the advantages and drawbacks for each of the method based on a comparison of the results performance. Besides that, this research focuses on various types of applications, emphasizing the importance of contrast enhancement for the improvement of its performance, especially in terms of accuracy and sensitivity. Previous studies were reviewed and critically compared to gain a better understanding of image enhancement. New ideas for further research improvement in image enhancement were proposed.
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paper_title: Self-Organizing Maps and Ancient Documents

paper_content:

This paper presents how Self-Organizing Maps and especially Kohonen maps can be applied to digital images of ancient collections in the perspective of valorization and diffusion. As an illustration, a scheme of transparency reduction of the digitized Gutenberg Bible is presented. In this two steps method, the Kohonen map is trained to generate a set of test vectors that will train in a supervised manner a classical feed-forward network. The testing step consists then in classifying each pixel into one class out of four by feeding directly the feed forward network. The pixels belonging to the transparency class are then removed.
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paper_title: Ancient degraded document image binarization based on texture features

paper_content:

In this paper, we present a promising method for binarization of historical and degraded document images, based on texture features. The proposed method is an adaptive threshold-based. This latter is computed by using a descriptor based on a co-occurrence matrix. The proposed method is tested objectively, using DIBCO dataset degraded documents and subjectively, using a set of ancient degraded documents provided by a national library. The results are satisfactory and promising, and present an improvement to classical methods.
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paper_title: Structure extraction from texture via relative total variation

paper_content:

It is ubiquitous that meaningful structures are formed by or appear over textured surfaces. Extracting them under the complication of texture patterns, which could be regular, near-regular, or irregular, is very challenging, but of great practical importance. We propose new inherent variation and relative total variation measures, which capture the essential difference of these two types of visual forms, and develop an efficient optimization system to extract main structures. The new variation measures are validated on millions of sample patches. Our approach finds a number of new applications to manipulate, render, and reuse the immense number of "structure with texture" images and drawings that were traditionally difficult to be edited properly.
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paper_title: A novel hybrid approach to restore historical degraded documents

paper_content:

Old degraded historical documents carry various important information regarding our culture, economics etc. proper restoration of these documents is very necessary. After digitization of these documents there remain noises and other low resolution components. These affect the overall visual appearance of the documents. In this paper a novel approach is proposed to enhance ancient historical documents. To enhance these digital format documents a two way approach is considered. At first Particle Swarm Optimization (PSO) and bilateral filter is applied. At second level Non-Linear Enhancement with bilateral filter is applied. Both the approaches are then tested visually and quantitively to show the effectiveness of the approach.
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paper_title: Character Shape Restoration of Binarized Historical Documents by Smoothing via Geodesic Morphology

paper_content:

We propose a method which performs an isotropic morphological dilation via implicit smoothing for the purpose of restoring the degraded character shapes of binarized images. Exploiting the idea of geodesic morphology that the binary image and its distance transformed image are interconvertible, we apply a smoothing method not to the binary image but to the distance transformed image, and then reconvert it by binarization. This allows us to apply conventional smoothing methods for continuous intensity, i.e., gray scale, images to the discrete intensity, i.e., binary, image implicitly. For instance, by using an isotropic diffusion together with geodesic dilation, an isotropic dilation along the stroke direction is obtained and brings better results.
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paper_title: Text Localization in Video Data Using DiscreteWavelet Transform

paper_content:

Text provides important information about images or video sequences in a documented image, but it always remains difficult to modify the static documented image. To carry out modification in any of the text matter the text must be segmented out from the documented image, which can be used for further analysis. Taking consideration to video image sequence the isolation of text data from the isolated frame becomes more difficult due to its variable nature. Various methods were proposed for the isolation of text data from the documented image. Among which Wavelet transforms have been widely used as effective tool in text segmentation. Document images usually contain three types of texture information. various wavelet transformation have been proposed for the decomposition of these images into their fundamentals feature. Onto these wavelet families, it is one of the difficult tasks in selecting a proper wavelet transformation with proper scale level for text isolation. This paper work implements an efficient text isolation algorithm for the extraction of text data from the documented video clips. The implemented system carries out a performance analysis on various wavelet transforms for the proper selection of wavelet transform with multi level decomposition. Of the selected wavelet transform the obtained wavelet a coefficient are applied with morphological operators for text isolation and evaluates the contribution of decomposition levels and wavelet functions to the segmentation result in documented video image. The proposed task implements neural network for the recognition of text characters from the isolated text image for making it.
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paper_title: Image Enhancement Technique on Contrast Variation: A Comprehensive Review

paper_content:

Image enhancement is very important, especially for the analysis and diagnosis of detailed information. Most of the studies conducted in image enhancement focus on contrast normalization. Generally, contrast determines how information in images can be perceived easily, how various details in the image can be easy distinguished or how objects of interest can be located. In this paper, a comprehensive review of image enhancement based on spatial domain (Histogram Equalization (HE) and Homomorphic Filtering) and frequency domain (Discrete Wavelet Transform (DWT)) is presented. The improvement and modification of methods were explained systematically. The objective of this work was to study the advantages and drawbacks for each of the method based on a comparison of the results performance. Besides that, this research focuses on various types of applications, emphasizing the importance of contrast enhancement for the improvement of its performance, especially in terms of accuracy and sensitivity. Previous studies were reviewed and critically compared to gain a better understanding of image enhancement. New ideas for further research improvement in image enhancement were proposed.
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paper_title: Basic Methods for Image Restoration and Identification

paper_content:

Publisher Summary This chapter describes the methods for restoration and identification of image. Many types of motion blur can be distinguished all of which are due to relative motion between the recording device and the scene. This can be in the form of a translation, rotation, sudden change of scale, or some combination of these. The improvement in signal-to-noise ratio is basically a measure that expresses the reduction of disagreement with the ideal image when comparing the distorted and restored image. When applying restoration filters to real images of which the ideal image is not available, often only the visual judgment of the restored image can be relied upon. For this reason it is desirable for a restoration filter to be somewhat tunable to the liking of the user. In many practical cases the actual restoration process has to be preceded by the identification of the point spread function (PSF). A more common situation is that the blur is estimated from the observed image itself. The blur identification procedure starts out by choosing a parametric model for the PSF. Then, the parametric blur model describes the PSF as a (small) set of coefficients within a given finite support. Within this support, the value of the PSF coefficients needs to be estimated.
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paper_title: An Effective Algorithm for Ink-Bleed through Removal in Document Images

paper_content:

When documents are written on both sides, quite often ink bleeds through the paper. This is a common phenomenon with old documents and low quality paper. With the presence of increased bleed-through, reading and deciphering the text becomes tedious. In this paper two methods are proposed for ink–bleed through removal. The first method is founded on Blind Segmentation Approach which exploits information from one side of the document and second method is based on the exploitation of information from both sides of the scanned document and founded on a registration process which aims at working on both the recto and verso sides simultaneously. These two methods are further compared with Otsu’s method which performs enhancement through thresholding.
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paper_title: Recovery of blurring scanned manuscript image based on wavelets transform algorithm

paper_content:

During paper manuscript scanning or photographing, which is the basis of automatic data entry for digitized documents by Optical Character Recognition (OCR) technique, the digital image blur is performed for scanning too thin or ink-bleed piece of paper and can usually cause OCR recognition errors. In this article, the combined algorithm of wavelet transform analysis and the median filter as well as the histogram adjustment is proposed for recovering Chinese character signal from the blurred mixtures. Scanning image of a piece of book page printed on thin paper-base is used to examine the algorithm, with the scanning image of the Chinese character mixed with the reverse side chart of the paper as blur signal. Simulation indicates that the combined algorithm can more effectively recover the blurring manuscript image to accuracy rate of 45% than the original image of accuracy rate only 1%. The combined algorithm proposed in this article can be directly integrated in OCR software to obtain higher accuracy for digital character recognition and automatic data entry.
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paper_title: Text Localization in Video Data Using DiscreteWavelet Transform

paper_content:

Text provides important information about images or video sequences in a documented image, but it always remains difficult to modify the static documented image. To carry out modification in any of the text matter the text must be segmented out from the documented image, which can be used for further analysis. Taking consideration to video image sequence the isolation of text data from the isolated frame becomes more difficult due to its variable nature. Various methods were proposed for the isolation of text data from the documented image. Among which Wavelet transforms have been widely used as effective tool in text segmentation. Document images usually contain three types of texture information. various wavelet transformation have been proposed for the decomposition of these images into their fundamentals feature. Onto these wavelet families, it is one of the difficult tasks in selecting a proper wavelet transformation with proper scale level for text isolation. This paper work implements an efficient text isolation algorithm for the extraction of text data from the documented video clips. The implemented system carries out a performance analysis on various wavelet transforms for the proper selection of wavelet transform with multi level decomposition. Of the selected wavelet transform the obtained wavelet a coefficient are applied with morphological operators for text isolation and evaluates the contribution of decomposition levels and wavelet functions to the segmentation result in documented video image. The proposed task implements neural network for the recognition of text characters from the isolated text image for making it.
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paper_title: Image Segmentation of Historical Handwriting from Palm Leaf Manuscripts

paper_content:

Palm leaf manuscripts were one of the earliest forms of written media and were used in Southeast Asia to store early written knowledge about subjects such as medicine, Buddhist doctrine and astrology. Therefore, historical handwritten palm leaf manuscripts are important for people who like to learn about historical documents, because we can learn more experience from them. This paper presents an image segmentation of historical handwriting from palm leaf manuscripts. The process is composed of three steps: 1) background elimination to separate text and background by Otsu’s algorithm 2) line segmentation and 3) character segmentation by histogram of image. The end result is the character’s image. The results from this research may be applied to optical character recognition (OCR) in the future.
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paper_title: Line separation for complex document images using fuzzy runlength

paper_content:

A new text line location and separation algorithm for complex handwritten documents is proposed. The algorithm is based on the application of a fuzzy directional runlength. The proposed technique was tested on a variety of complex handwritten document images including postal parcel images and historical handwritten documents such as Newton's and Galileo's manuscripts. A preliminary testing showed a successful rate of 93% of the test set.
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paper_title: Restoration of archival documents using a wavelet technique

paper_content:

This paper addresses a problem of restoring handwritten archival documents by recovering their contents from the interfering handwriting on the reverse side caused by the seeping of ink. We present a novel method that works by first matching both sides of a document such that the interfering strokes are mapped with the corresponding strokes originating from the reverse side. This facilitates the identification of the foreground and interfering strokes. A wavelet reconstruction process then iteratively enhances the foreground strokes and smears the interfering strokes so as to strengthen the discriminating capability of an improved Canny edge detector against the interfering strokes. The method has been shown to restore the documents effectively with average precision and recall rates for foreground text extraction at 84 percent and 96 percent, respectively.
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paper_title: Separating text and background in degraded document images - a comparison of global thresholding techniques for multi-stage thresholding

paper_content:

Before any processing of the textual content of a document image can be performed the text must be separated from the background of the image. Several thresholding algorithms have previously been proposed and are widely used in document processing. None have been shown effective at thresholding difficult documents where the background and foreground are non-uniform. In this paper we investigate the use of three global thresholding algorithms (Otsu's, Kapur's entropy and Solihin's quadratic integral ratio (QIR)) as the first stage in a multi-stage thresholding algorithm for use in degraded document images. It is concluded that Otsu's and Kapur's algorithms do not work well for difficult documents as they tend to over-threshold the image, thus losing much of the useful information. The QIR algorithm is more accurate in separating the foreground and background in these images, leaving a range of undecided, fuzzy, pixels for later processing in a subsequent stage.
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paper_title: Extracting a focused image from several out of focus micromechanical structure images

paper_content:

An all-focused image of a micromechanical structure can be extracted from several microscopic images with different focal planes. Spreading of the out-of-focus regions over the background, which shows the out-of-focus object bigger than its actual size, causes errors when a sharpness function is applied. The focused area with low contrast is also one of the difficulties faced in order to easily distinguish them from the out-of-focus regions. We propose a method to estimate the focused section of each image and create a final focused image by concatenating these areas. We let the size of the tested areas vary in order to obtain a more accurate result. The method finds focused pixels among the images using a voting method and pastes them to the final image. This approach does not rely on any information of the microscope. No a priori knowledge of an optical system is needed.
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paper_title: Flexible Text Recovery from Degraded Typewritten Historical Documents

paper_content:

The conversion of large collections of historical typewritten documents into digital libraries and archives is met with significant challenges that standard recognition techniques cannot address. The condition and individual nature of characters in these degraded documents necessitate a departure from existing thresholding approaches. This paper presents a flexible approach designed to overcome the difficulties presented by such documents by flexibly analysing each individual character and cautiously repairing it. The main sources of OCR errors are successfully addressed and reliable corrective actions are taken.
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paper_title: ISauvola: Improved Sauvola’s Algorithm for Document Image Binarization

paper_content:

Binarization of historical documents is difficult and is still an open area of research. In this paper, a new binarization technique for document images is presented. The proposed technique is based on the most commonly used binarization method: Sauvola’s, which performs relatively well on classical documents, however, three main defects remain: the window parameter of Sauvola’s formula does not fit automatically to the image content, is not robust to low contrasts, and not invariant with respect to contrast inversion. Thus on documents such as magazines, the content may not be retrieved correctly. In this paper we use the image contrast that is defined by the local image minimum and maximum in combination with the computed Sauvola’s binarization step to guarantee good quality binarization for both low and correctly contrasted objects inside a single document, without adjusting manually the user-defined parameters to the document content.
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paper_title: Cancellation of show-through in duplex scanning

paper_content:

When scanning a page with printing on both sides, the printing on the back-side often shows through in the scan of the front-side because the page is not completely opaque. This phenomenon of show-through is analyzed and an image processing method for removal of this commonly encountered degradation is developed. A simplified mathematical model is obtained from first physical principles. The model is linearized using suitable transformations and simplifying approximations. Based on the linearized model, an adaptive linear-filtering scheme is developed for the electronic removal of show-through using scans of both sides of the document. Experimental results demonstrating the effectiveness of the method developed are presented.
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paper_title: A background correction method for particle image under non-uniform illumination conditions

paper_content:

The limitation of imaging condition in actual environment often causes the non-uniform background light on the images during the course of capturing. In order to solve the problem, this paper proposed a method based on homomorphic filtering after discussing the characteristic of various common algorithms. It carried out treatment on image frequency region space, weakened low frequency component and strengthened the high frequency component appropriately, so it can reduce the effects of uneven illumination background to what image brings about. The experiment proved that the proposed method is better than some traditional methods.
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paper_title: Difficult and urgent open problems in document image analysis for libraries

paper_content:

Open problems in document image analysis research and development relevant to digital libraries (DLs) are briefly described and their difficulty and urgency estimated. They are grouped according to the stage of processing, in the construction of DLs, where they first apply: image capture, early processing, content extraction and recognition, structure analysis, retrieval, & presentation; and in personal and interactive DLs.
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paper_title: Retrospective Illumination Correction of Retinal Images

paper_content:

A method for correction of nonhomogenous illumination based on optimization of parameters of B-spline shading model with respect to Shannon's entropy is presented. The evaluation of Shannon's entropy is based on Parzen windowing method (Mangin, 2000) with the spline-based shading model. This allows us to express the derivatives of the entropy criterion analytically, which enables efficient use of gradient-based optimization algorithms. Seven different gradient- and nongradient-based optimization algorithms were initially tested on a set of 40 simulated retinal images, generated by a model of the respective image acquisition system. Among the tested optimizers, the gradient-based optimizer with varying step has shown to have the fastest convergence while providing the best precision. The final algorithm proved to be able of suppressing approximately 70% of the artificially introduced non-homogenous illumination. To assess the practical utility of the method, it was qualitatively tested on a set of 336 real retinal images; it proved the ability of eliminating the illumination inhomogeneity substantially in most of cases. The application field of this method is especially in preprocessing of retinal images, as preparation for reliable segmentation or registration.
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paper_title: Structure extraction from texture via relative total variation

paper_content:

It is ubiquitous that meaningful structures are formed by or appear over textured surfaces. Extracting them under the complication of texture patterns, which could be regular, near-regular, or irregular, is very challenging, but of great practical importance. We propose new inherent variation and relative total variation measures, which capture the essential difference of these two types of visual forms, and develop an efficient optimization system to extract main structures. The new variation measures are validated on millions of sample patches. Our approach finds a number of new applications to manipulate, render, and reuse the immense number of "structure with texture" images and drawings that were traditionally difficult to be edited properly.
---
paper_title: A novel hybrid approach to restore historical degraded documents

paper_content:

Old degraded historical documents carry various important information regarding our culture, economics etc. proper restoration of these documents is very necessary. After digitization of these documents there remain noises and other low resolution components. These affect the overall visual appearance of the documents. In this paper a novel approach is proposed to enhance ancient historical documents. To enhance these digital format documents a two way approach is considered. At first Particle Swarm Optimization (PSO) and bilateral filter is applied. At second level Non-Linear Enhancement with bilateral filter is applied. Both the approaches are then tested visually and quantitively to show the effectiveness of the approach.
---
paper_title: Estimating image blur in the wavelet domain

paper_content:

In this paper, a wavelet based method is proposed to estimate the blur in an image using information contained in the image itself. We look at the sharpness of the sharpest edges in the blurred image, which contain information about the blurring. Specifically, a smoothness measure, the Lipschitz exponent, is computed for these sharpest edges. Its value is related to the variance of a gaussian point spread function. This value is only dependent on the blur in the image and not on the image contents. This allows us to estimate the variance of the blur directly from the image itself. With minor modifications, the method can be extended to other types of blur that are described by one parameter (airy disks, out-of-focus blur, …).
---
paper_title: Semantics-based content extraction in typewritten historical documents

paper_content:

This paper presents a flexible approach to extracting content from scanned historical documents using semantic information. The final electronic document is the result of a "digital historical document lifecycle" process, where the expert knowledge of the historian/archivist user is incorporated at different stages. Results show that such a conversion strategy aided by (expert) user-specified semantic information and which enables the processing of individual parts of the document in a specialised way, produces superior (in a variety of significant ways) results than document analysis and understanding techniques devised for contemporary documents.
---
paper_title: Categorization using semi-supervised clustering

paper_content:

Many applications require matching objects to a predefined, yet highly dynamic set of categories accompanied by category descriptions. We present a novel approach to solving this class of categorization problems by formulating it in a semi-supervised clustering framework. Text-based matching is performed to generate ldquosoftrdquo seeds, which are then used to guide clustering in the basic feature space. We introduce a new variation of the k-means algorithm, called Soft Seeded k-means, which can effectively incorporate seeds that are of varying degrees of confidence, while allowing for incomplete coverage of the pre-defined categories. The algorithm is applied to real-world data from a business analytics application, and we demonstrate that it leads to superior performance compared to previous approaches.
---
paper_title: Serialized k-means for adaptative color image segmentation application to document images and others

paper_content:

This paper introduces an adaptative segmentation system that was designed for color document image analysis. The method is based on the serialization of a k-means algorithm that is applied sequentially by using a sliding window over the image. During the window’s displacement, the algorithm reuses information from the clusters computed in the previous window and automatically adjusts them in order to adapt the classifier to any new local variation of the colors. To improve the results, we propose to define several different clusters in the color feature space for each logical class. We also reintroduce the user into the initialization step to define the number of classes and the different samples for each class. This method has been tested successfully on ancient color manuscripts, video images and multiple natural and non-natural images having heavy defects and showing illumination variation and transparency. The proposed algorithm is generic enough to be applied on a large variety of images for different purposes such as color image segmentation as well as binarization.
---
paper_title: Clear underwater vision

paper_content:

Underwater imaging is important for scientific research and technology, as well as for popular activities. We present a computer vision approach which easily removes degradation effects in underwater vision. We analyze the physical effects of visibility degradation. We show that the main degradation effects can be associated with partial polarization of light. We therefore present an algorithm which inverts the image formation process, to recover a good visibility image of the object. The algorithm is based on a couple of images taken through a polarizer at different orientations. As a by product, a distance map of the scene is derived as well. We successfully used our approach when experimenting in the sea using a system we built. We obtained great improvement of scene contrast and color correction, and nearly doubled the underwater visibility range.
---
paper_title: Improving image quality in poor visibility conditions using a physical model for contrast degradation

paper_content:

In daylight viewing conditions, image contrast is often significantly degraded by atmospheric aerosols such as haze and fog. This paper introduces a method for reducing this degradation in situations in which the scene geometry is known. Contrast is lost because light is scattered toward the sensor by the aerosol particles and because the light reflected by the terrain is attenuated by the aerosol. This degradation is approximately characterized by a simple, physically based model with three parameters. The method involves two steps: first, an inverse problem is solved in order to recover the three model parameters; then, for each pixel, the relative contributions of scattered and reflected flux are estimated. The estimated scatter contribution is simply subtracted from the pixel value and the remainder is scaled to compensate for aerosol attenuation. This paper describes the image processing algorithm and presents an analysis of the signal-to-noise ratio (SNR) in the resulting enhanced image. This analysis shows that the SNR decreases exponentially with range. A temporal filter structure is proposed to solve this problem. Results are presented for two image sequences taken from an airborne camera in hazy conditions and one sequence in clear conditions. A satisfactory agreement between the model and the experimental data is shown for the haze conditions. A significant improvement in image quality is demonstrated when using the contrast enhancement algorithm in conjuction with a temporal filter.
---
paper_title: An Adaptive Binarization Technique for Low Quality Historical Documents

paper_content:

Historical document collections are a valuable resource for human history. This paper proposes a novel digital image binarization scheme for low quality historical documents allowing further content exploitation in an efficient way. The proposed scheme consists of five distinct steps: a pre-processing procedure using a low-pass Wiener filter, a rough estimation of foreground regions using Niblack’s approach, a background surface calculation by interpolating neighboring background intensities, a thresholding by combining the calculated background surface with the original image and finally a post-processing step in order to improve the quality of text regions and preserve stroke connectivity. The proposed methodology works with great success even in cases of historical manuscripts with poor quality, shadows, nonuniform illumination, low contrast, large signal- dependent noise, smear and strain. After testing the proposed method on numerous low quality historical manuscripts, it has turned out that our methodology performs better compared to current state-of-the-art adaptive thresholding techniques.
---
paper_title: Similarity Evaluation and Shape Feature Extraction for Character Pattern Retrieval to Support Reading Historical Documents

paper_content:

We have many historical documents written in over 1,000 years ago. Shape features of character patterns on the documents are unstable or missing because most of the documents have been stained and degraded deeply. Digital archives of the documents with accurate character pattern retrieval methods are helpful for archaeologists and historians. In this paper, we propose a similarity evaluation method for character patterns with missing shape parts. It collaboratively works with non-linear normalization for such patterns, and modifies the templates for each trial of the retrieval efficiently. In the experiences using 4,911 Kanji (Chinese origin) character patterns from the Japanese historical documents called mokkans, the method shows improvements of the retrieval accuracy. Also, we present a simple implementation of gradient feature extraction to compare the chain code feature with the gradient feature in the retrieval. As the result, the gradient feature works better than the chain code feature.
---
paper_title: Character Shape Restoration of Binarized Historical Documents by Smoothing via Geodesic Morphology

paper_content:

We propose a method which performs an isotropic morphological dilation via implicit smoothing for the purpose of restoring the degraded character shapes of binarized images. Exploiting the idea of geodesic morphology that the binary image and its distance transformed image are interconvertible, we apply a smoothing method not to the binary image but to the distance transformed image, and then reconvert it by binarization. This allows us to apply conventional smoothing methods for continuous intensity, i.e., gray scale, images to the discrete intensity, i.e., binary, image implicitly. For instance, by using an isotropic diffusion together with geodesic dilation, an isotropic dilation along the stroke direction is obtained and brings better results.
---
paper_title: Survey of Contrast Enhancement Techniques based on Histogram Equalization

paper_content:

This Contrast enhancement is frequently referred to as one of the most important issues in image processing. Histogram equalization (HE) is one of the common methods used for improving contrast in digital images. Histogram equalization (HE) has proved to be a simple and effective image contrast enhancement technique. However, the conventional histogram equalization methods usually result in excessive contrast enhancement, which causes the unnatural look and visual artifacts of the processed image. This paper presents a review of new forms of histogram for image contrast enhancement. The major difference among the methods in this family is the criteria used to divide the input histogram. Brightness preserving Bi-Histogram Equalization (BBHE) and Quantized Bi-Histogram Equalization (QBHE) use the average intensity value as their separating point. Dual Sub-Image Histogram Equalization (DSIHE) uses the median intensity value as the separating point. Minimum Mean Brightness Error Bi-HE (MMBEBHE) uses the separating point that produces the smallest Absolute Mean Brightness Error (AMBE). Recursive Mean-Separate Histogram Equalization (RMSHE) is another improvement of BBHE. The Brightness preserving dynamic histogram equalization (BPDHE) method is actually an extension to both MPHEBP and DHE. Weighting mean-separated sub-histogram equalization (WMSHE) method is to perform the effective contrast enhancement of the digital image.
---
paper_title: New off-line Handwritten Signature Verification method based on Artificial Immune Recognition System

paper_content:

A new system for Handwritten Signature Verification is proposed.The Artificial Immune Recognition System is proposed to achieve the verification task.Two new features that are Gradient Local Binary Patterns and Longest Run Features are proposed.Results obtained on CEDAR and GPDS-100 corpuses reveal the effectiveness of the proposed methods. Natural Immune System offers many interesting features that inspired the design of Artificial Immune Systems (AIS) used to solve various problems of engineering and artificial intelligence. AIS are particularly successful in fault detection and diagnosis applications where anomalies such as errors and failures are assimilated to viruses that should be detected. Thereby, AIS seem suitable to automatically detect forgeries in signature verification systems. This paper proposes a novel method for off-line signature verification that is based on the Artificial Immune Recognition System (AIRS). For feature generation, two different descriptors are proposed to generate signature traits. The first is the Gradient Local Binary Patterns that estimates gradient features based on the LBP neighborhood. The second descriptor is the Longest Run Feature, which describes the signature topology by considering longest suites of text pixels. Performance evaluation is carried out on CEDAR and GPDS-100 datasets. The results obtained showed that the proposed system has promising performance and often comfortably outperforms the state of the art.
---
paper_title: Image Enhancement Technique on Contrast Variation: A Comprehensive Review

paper_content:

Image enhancement is very important, especially for the analysis and diagnosis of detailed information. Most of the studies conducted in image enhancement focus on contrast normalization. Generally, contrast determines how information in images can be perceived easily, how various details in the image can be easy distinguished or how objects of interest can be located. In this paper, a comprehensive review of image enhancement based on spatial domain (Histogram Equalization (HE) and Homomorphic Filtering) and frequency domain (Discrete Wavelet Transform (DWT)) is presented. The improvement and modification of methods were explained systematically. The objective of this work was to study the advantages and drawbacks for each of the method based on a comparison of the results performance. Besides that, this research focuses on various types of applications, emphasizing the importance of contrast enhancement for the improvement of its performance, especially in terms of accuracy and sensitivity. Previous studies were reviewed and critically compared to gain a better understanding of image enhancement. New ideas for further research improvement in image enhancement were proposed.
---
paper_title: An Objective Out-of-Focus Blur Measurement

paper_content:

This paper presents a new method for objective measurement of out-of-focus blur images. The essential idea is to derive the point spread function (PSF) of an out-of-focus blur image from the line spread function (LSF). In this method, it is assumed that at least one sharp edge could be detected. Then, the LSF can be constructed from the edge(s). This approach has fast speed as it works in spatial domain without complex Fourier transform or iterative computation. Moreover, a generic criterion for out-of-focus blur measurement is proposed and it can be applied to all images without depending on image contents. The experimental results validate the accuracy and efficiency of the proposed method
---
paper_title: Ancient degraded document image binarization based on texture features

paper_content:

In this paper, we present a promising method for binarization of historical and degraded document images, based on texture features. The proposed method is an adaptive threshold-based. This latter is computed by using a descriptor based on a co-occurrence matrix. The proposed method is tested objectively, using DIBCO dataset degraded documents and subjectively, using a set of ancient degraded documents provided by a national library. The results are satisfactory and promising, and present an improvement to classical methods.
---
paper_title: Minimum error thresholding

paper_content:

Abstract A computationally efficient solution to the problem of minimum error thresholding is derived under the assumption of object and pixel grey level values being normally distributed. The method is applicable in multithreshold selection.
---
paper_title: ICFHR2016 Handwritten Document Image Binarization Contest (H-DIBCO 2016)

paper_content:

H-DIBCO 2016 is the international Handwritten Document Image Binarization Contest organized in the context of ICFHR 2016 conference. The general objective of the contest is to identify current advances in document image binarization of handwritten document images using performance evaluation measures that are motivated by document image analysis and recognition requirements. This paper describes the contest details including the evaluation measures used as well as the performance of the 12 submitted methods along with a brief description of each method.
---
paper_title: An introduction to digital image processing

paper_content:

A new and distinct spur type apple variety which originated as a limb mutation of the standard winter banana apple tree (non-patented) is provided. This new apple variety possesses a vigorous compact and only slightly spreading growth habit and can be distinguished from its parent and the Housden spur type winter banana apple variety (non-patented). More specifically, the new variety forms more fruiting spurs per unit length on two and three year old wood than the standard winter banana apple tree and less spurs per unit length than the Housden spur type winter banana apple tree. Additionally, the new variety has the ability to heavily bear fruit having a whitish-yellow skin color with a sometimes slight scarlet red blush upon maturity which is substantially identical to that of the standard winter banana apple tree and which has substantially less skin russeting than the Housden spur type winter banana apple tree.
---
paper_title: Comparison of Niblack inspired binarization methods for ancient documents

paper_content:

In this paper, we present a new sliding window based local thresholding technique 'NICK' and give a detailed ::: comparison of some existing sliding-window based thresholding algorithms with our method. The proposed method aims ::: at achieving better binarization results, specifically, for ancient document images. NICK has been inspired from the ::: Niblack's binarization method and exhibits its robustness and effectiveness when evaluated on low quality ancient ::: document images.
---
paper_title: Combination of Document Image Binarization Techniques

paper_content:

Document image binarization has been studied for decades, and many practical binarization techniques have been proposed for different kinds of document images. However, many state-of-the-art methods are particularly suitable for the document images that suffer from certain specific type of image degradation or have certain specific type of image characteristics. In this paper, we propose a classification framework to combine different thresholding methods and produce better performance for document image binarization. Given the binarization results of some reported methods, the proposed framework divides the document image pixels into three sets, namely, foreground pixels, background pixels and uncertain pixels. A classifier is then applied to iteratively classify those uncertain pixels into foreground and background, based on the pre-selected froeground and background sets. Extensive experiments over different datasets including the Document Image Binarization Contest(DIBCO)2009 and Handwritten Document Image Binarization Competition(H-DIBCO)2010 show that our proposed framework outperforms most state-of-the-art methods significantly.
---
paper_title: A Hybrid Binarization Technique for Document Images

paper_content:

In this chapter, a binarization technique specifically designed for historical document images is presented. Existing binarization techniques focus either on finding an appropriate global threshold or adapting a local threshold for each area in order to remove smear, strains, uneven illumination etc. Here, a hybrid approach is presented that first applies a global thresholding technique and, then, identifies the image areas that are more likely to still contain noise. Each of these areas is re-processed separately to achieve better quality of binarization. Evaluation results are presented that compare our technique with existing ones and indicate that the proposed approach is effective, combining the advantages of global and local thresholding. Finally, future directions of our research are mentioned.
---
paper_title: Document image binarization using local features and Gaussian mixture modeling

paper_content:

In this paper, we address the document image binarization problem with a three-stage procedure. First, possible stains and general document background information are removed from the image through a background removal stage. The remaining misclassified background and character pixels are then separated using a Local Co-occurrence Mapping, local contrast and a two-state Gaussian Mixture Model. Finally, some isolated misclassified components are removed by a morphology operator. The proposed scheme offers robust and fast performance, especially for both handwritten and printed documents, which compares favorably with other binarization methods. Background removal technique based on adaptive median filtering and thresholdingA Local Co-occurrence Map with local contrast can distinguish between document text and document stains and background.Low complexity approach with fast and accurate binarization results
---
paper_title: A multi-scale framework for adaptive binarization of degraded document images

paper_content:

In this work, a multi-scale binarization framework is introduced, which can be used along with any adaptive threshold-based binarization method. This framework is able to improve the binarization results and to restore weak connections and strokes, especially in the case of degraded historical documents. This is achieved thanks to localized nature of the framework on the spatial domain. The framework requires several binarizations on different scales, which is addressed by introduction of fast grid-based models. This enables us to explore high scales which are usually unreachable to the traditional approaches. In order to expand our set of adaptive methods, an adaptive modification of Otsu's method, called AdOtsu, is introduced. In addition, in order to restore document images suffering from bleed-through degradation, we combine the framework with recursive adaptive methods. The framework shows promising performance in subjective and objective evaluations performed on available datasets.
---
paper_title: Unsupervised ensemble of experts (EoE) framework for automatic binarization of document images

paper_content:

In recent years, a large number of binarization methods have been developed, with varying performance generalization and strength against different benchmarks. In this work, to leverage on these methods, an ensemble of experts (EoE) framework is introduced, to efficiently combine the outputs of various methods. The proposed framework offers a new selection process of the binarization methods, which are actually the experts in the ensemble, by introducing three concepts: confident ness, endorsement and schools of experts. The framework, which is highly objective, is built based on two general principles: (i) consolidation of saturated opinions and (ii) identification of schools of experts. After building the endorsement graph of the ensemble for an input document image based on the confident ness of the experts, the saturated opinions are consolidated, and then the schools of experts are identified by thresholding the consolidated endorsement graph. A variation of the framework, in which no selection is made, is also introduced that combines the outputs of all experts using endorsement-dependent weights. The EoE framework is evaluated on the set of participating methods in the H-DIBCO'12 contest and also on an ensemble generated from various instances of grid-based Sauvola method with promising performance.
---
paper_title: Document Image Binarization with Fully Convolutional Neural Networks

paper_content:

Binarization of degraded historical manuscript images is an important pre-processing step for many document processing tasks. We formulate binarization as a pixel classification learning task and apply a novel Fully Convolutional Network (FCN) architecture that operates at multiple image scales, including full resolution. The FCN is trained to optimize a continuous version of the Pseudo F-measure metric and an ensemble of FCNs outperform the competition winners on 4 of 7 DIBCO competitions. This same binarization technique can also be applied to different domains such as Palm Leaf Manuscripts with good performance. We analyze the performance of the proposed model w.r.t. the architectural hyperparameters, size and diversity of training data, and the input features chosen.
---
paper_title: Enhancement and Cleaning of Handwritten Data by Using Neural Networks

paper_content:

In this work, artificial neural networks are used to clean and enhance scanned images for a handwritten recognition task. Multilayer perceptrons are trained in a supervised way using a set of simulated noisy images together with the corresponding clean images for the desired output. The neural network acquires the function of a desired enhancing method. The performance of this method has been evaluated for both noisy artificial and natural images. Objective and subjective methods of evaluation have shown a superior performance of the proposed method over other conventional enhancing and cleaning filters.
---
paper_title: Document Image Binarization Using Recurrent Neural Networks

paper_content:

In the context of document image analysis, image binarization is an important preprocessing step for other document analysis algorithms, but also relevant on its own by improving the readability of images of historical documents. While historical document image binarization is challenging due to common image degradations, such as bleedthrough, faded ink or stains, achieving good binarization performance in a timely manner is a worthwhile goal to facilitate efficient information extraction from historical documents. In this paper, we propose a recurrent neural network based algorithm using Grid Long Short-Term Memory cells for image binarization, as well as a pseudo F-Measure based weighted loss function. We evaluate the binarization and execution performance of our algorithm for different choices of footprint size, scale factor and loss function. Our experiments show a significant trade-off between binarization time and quality for different footprint sizes. However, we see no statistically significant difference when using different scale factors and only limited differences for different loss functions. Lastly, we compare the binarization performance of our approach with the best performing algorithm in the 2016 handwritten document image binarization contest and show that both algorithms perform equally well.
---
paper_title: Character and line extraction from color map images using a multi-layer neural network

paper_content:

Abstract A multi-layer neural network based technique is presented in this paper for extracting characters and lines from color geographic map images. In this method, a neural network is first trained with feature values at known character and line pixels and background pixels and then used for image classification. The image segmentation problem is treated as a pattern classification process and the neural network classifier is used to generate non-linear decision regions to separate the foreground and background of an image that may contain a number of non-uniform regions with different colors. The method has been verified to work well with experimental studies.
---
paper_title: A selectional auto-encoder approach for document image binarization

paper_content:

Abstract Binarization plays a key role in the automatic information retrieval from document images. This process is usually performed in the first stages of document analysis systems, and serves as a basis for subsequent steps. Hence it has to be robust in order to allow the full analysis workflow to be successful. Several methods for document image binarization have been proposed so far, most of which are based on hand-crafted image processing strategies. Recently, Convolutional Neural Networks have shown an amazing performance in many disparate duties related to computer vision. In this paper we discuss the use of convolutional auto-encoders devoted to learning an end-to-end map from an input image to its selectional output, in which activations indicate the likelihood of pixels to be either foreground or background. Once trained, documents can therefore be binarized by parsing them through the model and applying a global threshold. This approach has proven to outperform existing binarization strategies in a number of document types.
---
paper_title: A novel variational model for noise robust document image binarization

paper_content:

Abstract Binarization is a fundamental problem in document image analysis systems. Different from current thresholding techniques, a novel variational model is proposed for noise robust document image binarization in this work, which is inspired by two classical variational models that have been successfully applied in image segmentation and denoising. In our variational model, the energy functional consists of three terms: data fidelity term, binary classification term and regularization term. As a result, the proposed model is capable of performing image binarization and suppressing noise simultaneously. Concretely, we firstly design a variational model for the original document image, the minimizer of which is our desired binarization result. Secondly, the gradient descent flow equation of our model is derived by means of variational principle. Lastly, a simple finite difference scheme, time forward and space center difference, is employed to solve the gradient descent flow equation. Extensive experiments are conducted on three types of document image datasets to validate our model qualitatively and quantitatively. The experiment results not only demonstrate the effectiveness of our approach, but also verify its noise and illumination robustness.
---
paper_title: Foreground-Background Separation by Feed-forward Neural Networks in Old Manuscripts

paper_content:

Artificial Neural Networks (ANNs) are widely used techniques in image processing and pattern ::: recognition. Despite of their power in classification tasks, for pattern recognition, they show limited ::: applicability in the earlier stages such as the foreground-background separation (FBS). In this paper a ::: novel FBS technique based on ANN is applied on old documents with a variety of degradations. The ::: idea is to train the ANN on a set of pairs of original images and their respective ideal black and white ::: ones relying on global and local information. We ran several experiments on benchmark and synthetic ::: data and we obtained better results than state-of-the art methods.
---
paper_title: Insights on the Use of Convolutional Neural Networks for Document Image Binarization

paper_content:

Convolutional Neural Networks have systematically shown good performance in Computer Vision and in Handwritten Text Recognition tasks. This paper proposes the use of these models for document image binarization. The main idea is to classify each pixel of the image into foreground and background from a sliding window centered at the pixel to be classified. An experimental analysis on the effect of sensitive parameters and some working topologies are proposed using two different corpora, of very different properties: DIBCO and Santgall.
---
paper_title: Grid Long Short-Term Memory

paper_content:

This paper introduces Grid Long Short-Term Memory, a network of LSTM cells arranged in a multidimensional grid that can be applied to vectors, sequences or higher dimensional data such as images. The network differs from existing deep LSTM architectures in that the cells are connected between network layers as well as along the spatiotemporal dimensions of the data. The network provides a unified way of using LSTM for both deep and sequential computation. We apply the model to algorithmic tasks such as 15-digit integer addition and sequence memorization, where it is able to significantly outperform the standard LSTM. We then give results for two empirical tasks. We find that 2D Grid LSTM achieves 1.47 bits per character on the Wikipedia character prediction benchmark, which is state-of-the-art among neural approaches. In addition, we use the Grid LSTM to define a novel two-dimensional translation model, the Reencoder, and show that it outperforms a phrase-based reference system on a Chinese-to-English translation task.
---
paper_title: A two-stage binarization approach for document images

paper_content:

Binarization of a gray scale document image is one of the most important steps for automatic document processing. The paper presents a two-stage document image binarization approach. The approach applies a region based binarization technique first to the whole image and utilizes a neural network based binarization technique to those text blocks in which a good character segmentation cannot be achieved at the first stage. Experimental results on a number of document images show that our two-stage binarization approach performs better than other binarization techniques in terms of character segmentation quality and computing time.
---
paper_title: A systematic analysis of performance measures for classification tasks

paper_content:

This paper presents a systematic analysis of twenty four performance measures used in the complete spectrum of Machine Learning classification tasks, i.e., binary, multi-class, multi-labelled, and hierarchical. For each classification task, the study relates a set of changes in a confusion matrix to specific characteristics of data. Then the analysis concentrates on the type of changes to a confusion matrix that do not change a measure, therefore, preserve a classifier's evaluation (measure invariance). The result is the measure invariance taxonomy with respect to all relevant label distribution changes in a classification problem. This formal analysis is supported by examples of applications where invariance properties of measures lead to a more reliable evaluation of classifiers. Text classification supplements the discussion with several case studies.
---
paper_title: EVALUATION: FROM PRECISION, RECALL AND F-MEASURE TO ROC, INFORMEDNESS, MARKEDNESS & CORRELATION

paper_content:

Commonly used evaluation measures including Recall, Precision, F-Factor and Rand Accuracy are biased and should not be used without clear understanding of the biases, and corresponding identification of chance or base case levels of the statistic. Using these measures a system that performs worse in the objective sense of Informedness, can appear to perform better under any of these commonly used measures. We discuss several concepts and measures that reflect the probability that prediction is informed versus chance. Informedness and introduce Markedness as a dual measure for the probability that prediction is marked versus chance. Finally we demonstrate elegant connections between the concepts of Informedness, Markedness, Correlation and Significance as well as their intuitive relationships with Recall and Precision, and outline the extension from the dichotomous case to the general multi-class case. .
---
paper_title: Document image analysis for World War II personal records

paper_content:

Complete collections of invaluable documents of unique historical and political significance are decaying and at the same time they are virtually inaccessible, necessitating the invention of robust and efficient methods for their conversion into a searchable electronic form. We present the issues encountered and problems addressed in the MEMORIAL project, whose goal is the establishment of a digital document workbench enabling the creation of distributed virtual archives based on documents existing in libraries, archives, museums, memorials, and public record offices. Successful approaches are described in the context of the chosen data class: a variety of typewritten documents containing personal information relating to the presence of individuals in World War II Nazi concentration camps.
---
paper_title: PETS Metrics: On-Line Performance Evaluation Service

paper_content:

This paper presents the PETS Metrics On-line Evaluation Service for computational visual surveillance algorithms. The service allows researchers to submit their algorithm results for evaluation against a set of applicable metrics. The results of the evaluation processes are publicly displayed allowing researchers to instantly view how their algorithm performs against previously submitted algorithms. The approach has been validated using seven motion segmentation algorithms.
---
paper_title: Distance-reciprocal distortion measure for binary document images

paper_content:

In this letter, we present a novel objective distortion measure for binary document images. This measure is based on the reciprocal of distance that is straightforward to calculate. Our results show that the proposed distortion measure matches well to subjective evaluation by human visual perception.
---
paper_title: A general system for the retrieval of document images from digital libraries

paper_content:

Large collections of scanned documents (books and journals) are now available in digital libraries. The most common method for retrieving relevant information from these collections is image browsing, but this approach is not feasible for books with more than a few dozen pages. The recognition of printed text can be made on the images by OCR systems, and in this case a retrieval by textual content can be performed. However, the results heavily depend on the quality of original documents. More sophisticated navigation can be performed when an electronic table of contents of the book is available with links to the corresponding pages. An opposite approach relies on the reduction of the amount of symbolic information to be extracted at the storage time. This approach is taken into account by document image retrieval systems. We describe a system that we developed in order to retrieve information from digitized books and journals belonging to digital libraries. The main feature of the system is the ability of combining two principal retrieval strategies in several ways. The first strategy allows an user to find pages with a layout similar to a query page. The second strategy is used in order to retrieve words in the collection matching a user-defined query, without performing OCR. The combination of these basic strategies allows users to retrieve meaningful pages with a low effort during the indexing phase. We describe the basic tools used in the system (layout analysis, layout retrieval, word retrieval) and the integration of these tools for answering complex queries. The experimental results are made on 1287 pages and show the effectiveness of the integrated retrieval.
---
paper_title: Fast handwriting recognition for indexing historical documents

paper_content:

Handwriting recognition (HR) has been successfully used in several applications such as postal address interpretation [S. Srihari et al., (1997)], bank check reading [S. Impedovo et al., (1997)], and forms reading [S. Madhvanath et al., (1995)]. These applications are all characterized by small or fixed lexicons afforded by contextual knowledge. Machine recognition of handwriting in historical documents presents two primary challenges: (i) large lexicons (over 10000 words) leading to low recognition accuracy (less than 50%) and (ii) a need for high speed HR given the millions of handwritten manuscripts in digital library repositories and that the speed is usually inversely proportional to lexicon size. We address the issue of speed when dealing with large lexicons. We present several techniques to improve the processing speed for a gain of up to 7 times in matching time and describe a method whereby the large lexicon is divided into smaller sets and processed in parallel. With 4 processors 18 times speedup for the matching phase is achieved.
---
paper_title: Review on image enhancement methods of old manuscript with the damaged background

paper_content:

Quite often that old documents are suffering from background damage. Examples of background damages are varying contrast, ancient document age and the documents have degraded over time due to storage conditions and the quality of the written parchment. These have damaged background for example such as: have varying contrast, smudges, dirty, presence of seeping ink from the other side of the document, uneven background. In order to make them readable, image processing offers a selection of approaches. The aim of this paper is to provide comprehensive review methods to enhance old document images with damaging background. Three kinds of enhancement methods are: (a) Image enhancement methods using binarization method or thresholding method, (b) Image enhancement methods using binarization method or thresholding method and other methods, (c) Image enhancement methods using other methods only. As conclusion, the second method has becoming more popular and has a great potential to improve in future.
---
paper_title: Difficult and urgent open problems in document image analysis for libraries

paper_content:

Open problems in document image analysis research and development relevant to digital libraries (DLs) are briefly described and their difficulty and urgency estimated. They are grouped according to the stage of processing, in the construction of DLs, where they first apply: image capture, early processing, content extraction and recognition, structure analysis, retrieval, & presentation; and in personal and interactive DLs.
---
paper_title: Document Image Quality Assessment: A Brief Survey

paper_content:

To maintain, control and enhance the quality of document images and minimize the negative impact of degradations on various analysis and processing systems, it is critical to understand the types and sources of degradations and develop reliable methods for estimating the levels of degradations. This paper provides a brief survey of research on the topic of document image quality assessment. We first present a detailed analysis of the types and sources of document degradations. We then review techniques for document image degradation modeling. Finally, we discuss objective measures and subjective experiments that are used to characterize document image quality.
---
paper_title: Pixel-wise binarization of musical documents with convolutional neural networks

paper_content:

Binarization is an important process in document analysis systems. Yet, it is quite difficult to devise a binarization method that perform successfully over a wide range of documents, especially in the case of digitized old musical manuscripts and scores with irregular lighting and source degradation. Our approach to binarization of musical documents is based on training a Convolutional Neural Network that classifies each pixel of the image as either background or foreground. Our results demonstrate that the approach is competitive with other state-of-the-art algorithms. It also illustrates the advantage of being able to adapt to any type of score by simply modifying the training set.
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paper_title: Document Image Quality Assessment: A Brief Survey

paper_content:

To maintain, control and enhance the quality of document images and minimize the negative impact of degradations on various analysis and processing systems, it is critical to understand the types and sources of degradations and develop reliable methods for estimating the levels of degradations. This paper provides a brief survey of research on the topic of document image quality assessment. We first present a detailed analysis of the types and sources of document degradations. We then review techniques for document image degradation modeling. Finally, we discuss objective measures and subjective experiments that are used to characterize document image quality.
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paper_title: Document image binarization using background estimation and stroke edges

paper_content:

Document images often suffer from different types of degradation that renders the document image binarization a challenging task. This paper presents a document image binarization technique that segments the text from badly degraded document images accurately. The proposed technique is based on the observations that the text documents usually have a document background of the uniform color and texture and the document text within it has a different intensity level compared with the surrounding document background. Given a document image, the proposed technique first estimates a document background surface through an iterative polynomial smoothing procedure. Different types of document degradation are then compensated by using the estimated document background surface. The text stroke edge is further detected from the compensated document image by using L1-norm image gradient. Finally, the document text is segmented by a local threshold that is estimated based on the detected text stroke edges. The proposed technique was submitted to the recent document image binarization contest (DIBCO) held under the framework of ICDAR 2009 and has achieved the top performance among 43 algorithms that are submitted from 35 international research groups.
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paper_title: Adaptive document binarization

paper_content:

A new method is presented for adaptive document image binarization, where the page is considered as a collection of subcomponents such as text, background and picture. The problems caused by noise, illumination and many source type related degradations are addressed. The algorithm uses document characteristics to determine (surface) attributes, often used in document segmentation. Using characteristic analysis, two new algorithms are applied to determine a local threshold for each pixel. An algorithm based on soft decision control is used for thresholding the background and picture regions. An approach utilizing local mean and variance of gray values is applied to textual regions. Tests were performed with images including different types of document components and degradations. The results show that the method adapts and performs well in each case.
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paper_title: An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows

paper_content:

Binary image representation is essential format for document analysis. In general, different available binarization techniques are implemented for different types of binarization problems. The majority of binarization techniques are complex and are compounded from filters and existing operations. However, the few simple thresholding methods available cannot be applied to many binarization problems. In this paper, we propose a local binarization method based on a simple, novel thresholding method with dynamic and flexible windows. The proposed method is tested on selected samples called the DIBCO 2009 benchmark dataset using specialized evaluation techniques for binarization processes. To evaluate the performance of our proposed method, we compared it with the Niblack, Sauvola and NICK methods. The results of the experiments show that the proposed method adapts well to all types of binarization challenges, can deal with higher numbers of binarization problems and boosts the overall performance of the binarization.
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paper_title: Adaptive Binarization of Historical Document Images

paper_content:

In this paper, we present a binarization technique specifically designed for historical document images. Existing methods for this problem focus on either finding a good global threshold or adapting the threshold for each area so that to remove smear, strains, uneven illumination etc. We propose a hybrid approach that first applies a global thresholding method and, then, identifies the image areas that are more likely to still contain noise. Each of these areas is re-processed separately to achieve better quality of binarization. We evaluate the proposed approach for different kinds of degradation problems. The results show that our method can handle hard cases while documents already in good condition are not affected drastically.
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paper_title: Minimum cross entropy thresholding

paper_content:

Abstract The threshold selection problem is solved by minimizing the cross entropy between the image and its segmented version. The cross entropy is formulated in a pixel-to-pixel basis between the two images and a computationally attractive algorithm employing the histogram is developed. Without making a priori assumptions about the population distribution, this method provides an unbiased estimate of a binarized version of the image in an information theoretic sense.
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paper_title: A novel image binarization method using hybrid thresholding

paper_content:

In this paper, we proposed a simple yet effective image binarization method to work with various kinds of images, such as images with the color bleeding characters, or with non-uniform background exposed to poor ambient light, and so forth. Our approach is based on a hybrid color quantization process, which adaptively takes the global and local image characteristics into account; therefore, it can deal with complex images. The experiment result shows that our proposed hybrid method outperforms the literature methods.
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paper_title: A local thresholding algorithm for images of handwritten historical documents

paper_content:

This paper presents a new algorithm to threshold images of historical documents. The thresholding phase is one of the most important phase in document recognition. An error on it could turn impossible the recognition of the document content. The proposed algorithm is divided into three phases. The first is responsible to indentify the main objects of the image. The second phase divides the image into sub-images according to the previous identification. And the latest phase evaluates a local threshold for each sub-image and proceeds with the binarization of each region. Our approach presented good results in images with complex background and it obtained the best performace when compared with other thresholding algorithms based on the measures used in DIBCO 2009.
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paper_title: Adaptive thresholding algorithm: Efficient computation technique based on intelligent block detection for degraded document images

paper_content:

Document image binarization involves converting gray level images into binary images, which is a feature that has significantly impacted many portable devices in recent years, including PDAs and mobile camera phones. Given the limited memory space and the computational power of portable devices, reducing the computational complexity of an embedded system is of priority concern. This work presents an efficient document image binarization algorithm with low computational complexity and high performance. Integrating the advantages of global and local methods allows the proposed algorithm to divide the document image into several regions. A threshold surface is then constructed based on the diversity and the intensity of each region to derive the binary image. Experimental results demonstrate the effectiveness of the proposed method in providing a promising binarization outcome and low computational cost.
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paper_title: Document binarization with automatic parameter tuning

paper_content:

Document analysis systems often begin with binarization as a first processing stage. Although numerous techniques for binarization have been proposed, the results produced can vary in quality and often prove sensitive to the settings of one or more control parameters. This paper examines a promising approach to binarization based upon simple principles, and shows that its success depends most significantly upon the values of two key parameters. It further describes an automatic technique for setting these parameters in a manner that tunes them to the individual image, yielding a final binarization algorithm that can cut total error by one-third with respect to the baseline version. The results of this method advance the state of the art on recent benchmarks.
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paper_title: Threshold selection based on fuzzy c-partition entropy approach

paper_content:

Abstract Thresholding is an important topic for image processing, pattern recognition and computer vision. Selecting thresholds is a critical issue for many applications. The fuzzy set theory has been successfully applied to many areas, such as control, image processing, pattern recognition, computer vision, medicine, social science, etc. It is generally believed that image processing bears some fuzziness in nature. In this paper, we use the concept of fuzzy c -partition and the maximum fuzzy entropy principle to select threshold values for gray-level images. We have conducted experiments on many images. The experimental results demonstrate that the proposed approach can select the thresholds automatically and effectively, and the resulting images can preserve the main features of the components of the original images very well.
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Title: Degraded Historical Document Binarization: A Review on Issues, Challenges, Techniques, and Future Directions

Section 1: Introduction
Description 1: This section introduces the importance of ancient and historic documents, the issues causing degradation over time, and the necessity of converting these documents into digital form using OCR and binarization techniques.

Section 2: Challenges in Historical Documents
Description 2: This section discusses the common issues faced by historical documents leading to degradation, which impacts their readability and digitization.

Section 3: Uneven Illumination
Description 3: This section explains how uneven illumination in historical documents impacts the accuracy of OCR and binarization methods.

Section 4: Contrast Variation
Description 4: This section addresses the problem of contrast variation in historical documents and its effect on document image analysis and binarization.

Section 5: Bleed-Through Degradation
Description 5: This section covers the issue of bleed-through degradation, where ink from one side of the paper affects the text on the other side, posing challenges for document binarization.

Section 6: Faded Ink or Faint Characters
Description 6: This section discusses the degradation of ink or faint characters in typewritten and handwritten documents, making recognition difficult.

Section 7: Smear or Show Through
Description 7: This section highlights the problem of smear or show-through where text from the reverse side of the document interferes with the front side, affecting readability.

Section 8: Blur
Description 8: This section elaborates on the types of blur (motion and out-of-focus) affecting historical documents and the challenges they pose for OCR.

Section 9: Thin or Weak Text
Description 9: This section focuses on the issue of thin or weak text in historical documents due to ink degradation or quality, impacting binarization accuracy.

Section 10: Deteriorated Documents
Description 10: This section explores the various factors contributing to the deterioration of physical documents and their implications on digitization efforts.

Section 11: Handling Historical Document Degradation Issues
Description 11: This section reviews the techniques used to handle various degradation issues in historical documents prior to binarization.

Section 12: Document Binarization
Description 12: This section introduces the process of document binarization and the importance of separating foreground text from the degraded background.

Section 13: Degraded Document Binarization Methods
Description 13: This section discusses the different methodologies used for binarizing degraded documents, categorized into global, local, and hybrid thresholding approaches.

Section 14: Global Thresholding-Based Binarization Methods
Description 14: This section provides an overview of global thresholding methods for binarization, including commonly used techniques like Otsu's method.

Section 15: Local/Adaptive Thresholding-Based Binarization Methods
Description 15: This section covers local or adaptive thresholding methods, including algorithms like Niblack's and Sauvola's methods.

Section 16: Hybrid Thresholding-Based Binarization Methods
Description 16: This section explains hybrid thresholding methods that combine global and local thresholding techniques to improve binarization results.

Section 17: Machine Learning-Based Binarization Methods
Description 17: This section discusses the application of machine learning algorithms, including neural networks, for binarizing degraded document images.

Section 18: Performance Metrics and Advanced Document Binarization
Description 18: This section introduces performance metrics used to evaluate binarization methods and discusses advanced approaches to document binarization.

Section 19: Performance Metrics to Evaluate Binarization Methods
Description 19: This section elaborates on various metrics like F-Measure, PSNR, NRM, and others used to assess the effectiveness of binarization techniques.

Section 20: Advances in Binarization Methods
Description 20: This section reviews recent advances in binarization methods for dealing with different kinds of degradation in historical documents.

Section 21: Conclusions
Description 21: This section summarizes the reviewed issues, challenges, and techniques in degraded document binarization, and discusses future directions for research in this area.