Document Image Quality Assessment: A Brief Survey
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paper_title: Characterization of Image Degradation Caused by Scanning

paper_content:

Abstract A single parameter value that represents the difference between the original and the digitized characters is determined from a binary scan of a test chart. It represents the combined effect of the point spread function diameter and the binary intensity threshold and is used to generate synthetic characters that are similar to characters scanned under the same conditions as the test chart.
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paper_title: A Taxonomy for Noise in Images of Paper Documents - The Physical Noises

paper_content:

A taxonomy encompasses not only a classification methodology, but also an explicative theory of the phenomena that justify such classification. This paper introduces a taxonomy for noise in images of paper documents and details the Physical Noises according to the proposed taxonomy.
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paper_title: A two-state Markov chain model of degraded document images

paper_content:

We propose a two-state Markov chain model of degraded document images. The model generates random and burst noise to simulate isolated pixel reversal as well as blurring of a larger document region. In the random state, the probability of pixel inversion is low compared to that in the burst state. However, the model remains in the random state for a much longer period of time. Validation of the model has been done using the statistical methodology of (Kanungo et al., 1995). To estimate the parameters efficiently, we use a genetic algorithm (GA) to search through the parameter space in which the model parameter values are encoded into a concatenated bit string to form the chromosomes. We also show how the accuracy of an optical character recognition system with dictionary search varies with two derived parameters of the proposed noise model.
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paper_title: Human Image Preference and Document Degradation Models

paper_content:

Because most degraded documents are created by people, the preferences individuals have in relation to degraded documents are quite important. Their preferences may determine whether or not the documents they created are appropriate for machines. The goal of this study was to find relationships between preference and several parameters of a scanner degradation model. It was found that the difference in binarization threshold and the difference in edge displacement caused by the degradation both had strong linear relationships to preference. The width of the point spread function did not show such a relationship. These relationships were counterintuitive because degraded characters with thicker stroke widths than the original were preferred to those that had stroke widths closer to the original character.
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paper_title: Global and local document degradation models

paper_content:

Two sources of document degradation are modeled: i) perspective distortion that occurs while photocopying or scanning thick, bound documents, and ii) degradation due to perturbations in the optical scanning and digitization process: speckle, blurr, jitter, thresholding. Perspective distortion is modeled by studying the underlying perspective geometry of the optical system of photocopiers and scanners. An illumination model is described to account for the nonlinear intensity change occuring across a page in a perspective-distorted document. The optical distortion process is modeled morphologically. First, a distance transform on the foreground is performed, followed by a random inversion of binary pixels where the probability of flip is a function of the distance of the pixel to the boundary of the foreground. Correlating the flipped pixels is modeled by a morphological closing operation. >
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paper_title: Groundtruth Generation and Document Image Degradation

paper_content:

Abstract : The problem of generating synthetic data for the training and evaluation of document analysis systems has been widely addressed in recent years. With the increased interest in processing multilingual sources, however, there is a tremendous need to be able to rapidly generate data in new languages and scripts, without the need to develop specialized systems. We have developed a system, which uses language support of the MS Windows operating system combined with custom print drivers to render tiff images simultaneously with windows Enhanced Metafile directives. The metafile information is parsed to generate zone, line, word, and character ground truth including location, font information and content in any language supported by Windows. The resulting images can be physically or synthetically degraded by our degradation modules, and used for training and evaluating Optical Character Recognition (OCR) systems. Our document image degradation methodology incorporates several often-encountered types of noise at the page and pixel levels. Examples of OCR evaluation and synthetically degraded document images are given to demonstrate the effectiveness.
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paper_title: Document image defect models and their uses

paper_content:

The accuracy of today's document recognition algorithms falls abruptly when image quality degrades even slightly. In an effort to surmount this barrier, researchers have in recent years intensified their study of explicit, quantitative, parameterized models of the image defects that occur during printing and scanning. The author reviews the recent literature and discusses the form these models might take. A preview of a large public-domain database of character images, labeled with ground-truth including all defect model parameters, is given. The use of massive pseudo-randomly generated training sets for the construction of high-performance decision trees for preclassification is described. In a more theoretical vein, the author reports preliminary results in the estimation of the intrinsic error of precisely-specified text recognition problems. Finally, the author calls attention to some open problems. >
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paper_title: Super-resolution enhancement of text image sequences

paper_content:

The objective of this work is the super-resolution enhancement of image sequences. We consider in particular images of scenes for which the point-to-point image transformation is a plane projective transformation. We first describe the imaging model, and a maximum likelihood (ML) estimator of the super-resolution image. We demonstrate the extreme noise sensitivity of the unconstrained ML estimator. We show that the Irani and Peleg (1991, 1993) super-resolution algorithm does not suffer from this sensitivity, and explain that this stability is due to the error back-projection method which effectively constrains the solution. We then propose two estimators suitable for the enhancement of text images: a maximum a posteriori (MAP) estimator based on a Huber prior and an estimator regularized using the total variation norm. We demonstrate the improved noise robustness of these approaches over the Irani and Peleg estimator. We also show the effects of a poorly estimated point spread function (PSF) on the super-resolution result and explain conditions necessary for this parameter to be included in the optimization. Results are evaluated on both real and synthetic sequences of text images. In the case of the real images, the projective transformations relating the images are estimated automatically from the image data, so that the entire algorithm is automatic.
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paper_title: Characterization of Image Degradation Caused by Scanning

paper_content:

Abstract A single parameter value that represents the difference between the original and the digitized characters is determined from a binary scan of a test chart. It represents the combined effect of the point spread function diameter and the binary intensity threshold and is used to generate synthetic characters that are similar to characters scanned under the same conditions as the test chart.
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paper_title: Validation of Image Defect Models for Optical Character Recognition

paper_content:

Considers the problem of evaluating character image generators that model distortions encountered in optical character recognition (OCR). While a number of such defect models have been proposed, the contention that they produce the desired result is typically argued in an ad hoc and informal way. The authors introduce a rigorous and more pragmatic definition of when a model is accurate: they say a defect model is validated if the OCR errors induced by the model are indistinguishable from the errors encountered when using real scanned documents. The authors describe four measures to quantify this similarity, and compare and contrast them using over ten million scanned and synthesized characters in three fonts. The measures differentiate effectively between different fonts and different scans of the same font regardless of the underlying text.
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paper_title: A Statistical , Nonparametric Methodology for Document Degradation Model Validation

paper_content:

Printing, photocopying, and scanning processes degrade the image quality of a document. Statistical models of these degradation processes are crucial for document image understanding research. In this paper, we present a statistical methodology that can be used to validate local degradation models. This method is based on a nonparametric, two-sample permutation test. Another standard statistical device, the power function, is then used to choose between algorithm variables such as distance functions. Since the validation and the power function procedures are independent of the model, they can be used to validate any other degradation model. A method for comparing any two models is also described. It uses p-values associated with the estimated models to select the model that is closer to the real world.
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paper_title: Correlating degradation models and image quality metrics

paper_content:

OCR often performs poorly on degraded documents. One approach to improving performance is to determine a good filter to improve the appearance of the document image before sending it to the OCR engine. Quality metrics have been measured in document images to determine what type of filtering would most likely improve the OCR response for that document image. In this paper those same quality metrics are measured for several word images degraded by known parameters in a document degradation model. The correlation between the degradation model parameters and the quality metrics is measured. High correlations do appear in many places that were expected. They are also absent in some expected places and offer a comparison of quality metric definitions proposed by different authors.
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paper_title: Estimating degradation model parameters using neighborhood pattern distributions: an optimization approach

paper_content:

Noise models are crucial for designing image restoration algorithms, generating synthetic training data, and predicting algorithm performance. There are two related but distinct estimation scenarios. The first is model calibration, where it is assumed that the input ideal bitmap and the output of the degradation process are both known. The second is the general estimation problem, where only the image from the output of the degradation process is given. While researchers have addressed the problem of calibration of models, issues with the general estimation problems have not been addressed in the literature. In this paper, we describe a parameter estimation algorithm for a morphological, binary, page-level image degradation model. The inputs to the estimation algorithm are 1) the degraded image and 2) information regarding the font type (italic, bold, serif, sans serif). We simulate degraded images using our model and search for the optimal parameter by looking for a parameter value for which the local neighborhood pattern distributions in the simulated image and the given degraded image are most similar. The parameter space is searched using a direct search optimization algorithm. We use the p-value of the Kolmogorov-Smirnov test as the measure of similarity between the two neighborhood pattern distributions. We show results of our algorithm on degraded document images.
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paper_title: Printer Modeling for Document Imaging

paper_content:

The microscopic details of printing often are unnoticed by humans, but can make differences that affect machine recognition of printed text. Models of the defects introduced into images by printing can be used to improve machine recognition. A probabilistic model used to generate images showing toner placement bears similarities to actual printed images. An equation derived for the average coverage of paper by toner particles having probabilistic placement is developed using geometric probability. Simulations show that averages of 'printed images' do have the same average coverage as the derived average coverage equations.
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paper_title: Scanner parameter estimation using bilevel scans of star charts

paper_content:

Scanning a high-contrast image in bilevel mode results in image degradation. This is caused by two primary effects: blurring and thresholding. This paper expands on a method of estimating a joint distortion parameter called the edge spread, from a star sector test chart in order to calculate the values of the point spread function width and binarization threshold. This theory is also described for variations in the source pattern which can represent degradations caused by repetition of the bilevel process as would be seen in printing then scanning, or in repeated photocopying. Estimation results are shown for the basic and extended cases.
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paper_title: Statistical image differences, degradation features, and character distance metrics

paper_content:

Abstract.Document image quality is degraded through processes such as scanning, printing, and photocopying. The resulting bilevel image degradations can be categorized based either on observable degradation features or on degradation model parameters. The image degradation features can be related mathematically to model parameters. In this paper we statistically compare pairs of populations of degraded character images created with different model parameters. The probability that the character populations were degraded by the same model parameters correlates with the relationship between observable degradation features and the model parameters. Two metrics of character difference are used: Hamming distance and moment feature distance. Knowledge about the conditions under which characters will be similar and when they will be different can influence the choice of parameters for future experiments.
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paper_title: Power functions and their use in selecting distance functions for document degradation model validation

paper_content:

Two document degradation models that model the perturbations introduced during the document printing and scanning process were proposed recently. Although degradation models are very useful, it is very important that we validate these models by comparing the synthetically generated images against real images. In recent past, two different validation procedures have also been proposed to validate such document degradation models. These validation procedures are functions of sample size and various distance functions. In this paper we outline a statistical methodology to compare the various validation schemes that result by using different distance functions. This methodology is general enough to compare any two validation schemes.
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paper_title: Document image quality: making fine discriminations

paper_content:

We estimate, using synthetically generated images, the smallest changes in document image quality that can be distinguished reliably and fully automatically by T. Kanungo's (1996) bootstrapping test. Six parameters of a physics based document image degradation model (H.S. Baird, 1992), are varied, one at time: for each, over a range of parameter value differences, two sets of synthetic images are generated pseudorandomly and the two sets tested for statistical equivalence using Kanungo's method. The rate at which Kanungo's method rejects the hypothesis that the two sets are drawn from the same distribution is analyzed as a function of parameter difference (a specialized "power function"). The finest discriminations afforded by the method are given by the width of the power function at a low fixed reject threshold. The data show that remarkably fine discriminations are possible-often subtler than are evident to visual inspection-for all six parameters. As few as 25 reference images are sufficient. These results suggest that Kanungo's method is sufficiently sensitive to a wide range of physics based image degradations to serve as an engineering foundation for many image quality estimation and OCR engineering purposes.
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paper_title: Low quality document image modeling and enhancement

paper_content:

In order to tackle problems such as shadow- through and bleed-through, a novel defect model is developed which generates physically damaged document images. This model addresses physical degradation, such as aging and ink seepage. Based on the diffusive nature of the physical defects, the model is designed using virtual diffusion processes. Then, based on this degradation model, a restoration method is proposed and used to fix the bleed-through effect in double-sided document images using the reverse diffusion process. Subjective and objective evaluations are performed on both the degradation model and the restoration method. The experiments show promising results on both real and generated data.
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paper_title: Document image defect models

paper_content:

A lack of explicit quantitative models of imaging defects due to printing, optics, and digitization has retarded progress in some areas of document image analysis, including syntactic and structural approaches. Establishing the essential properties of such models, such as completeness (expressive power) and calibration (closeness of fit to actual image populations) remain open research problems. Work-in-progress towards a parameterized model of local imaging defects is described, together with a variety of motivating theoretical arguments and empirical evidence. A pseudo-random image generator implementing the model has been built. Applications of the generator are described, including a polyfont classifier for ASCII and a single-font classifier for a large alphabet (Tibetan U-Chen), both of which which were constructed with a minimum of manual effort. Image defect models and their associated generators permit a new kind of image database which is explicitly parameterized and indefinitely extensible, alleviating some drawbacks of existing databases.
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paper_title: Sorting qualities of handwritten Chinese characters for setting up a research database

paper_content:

Setting up a database containing a large volume of character samples is important for research on character recognition. As the character samples are collected, the samples must be classified into the correct category, it is also necessary to establish an index for measuring the quality of the samples so that one can give information about their quality when certain samples are mentioned. An automatic sorting procedure for Chinese characters is proposed based on the Bayes rule. Three features about Chinese characters are used for the sorting. The sorting results are checked by examining curves of the recognition rates for all samples as the samples are arranged with or without sorting. The experimental results show that the sorting method is effective. >
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paper_title: Sharpness estimation for document and scene images

paper_content:

Images of document pages have different characteristics than images of natural scenes, and so the sharpness measures developed for natural scene images do not necessarily extend to document images primarily composed of text. We present an efficient and simple method for effectively estimating the sharp-ness/blurriness of document images that also performs well on natural scenes. Our method can be used to predict the sharpness in scenarios where images are blurred due to camera-motion (or hand-shake), defocus, or inherent properties of the imaging system. The proposed method outperforms the perceptually-based, no-reference sharpness work of [1] and [4], which was shown to perform better than 14 other no-reference sharpness measures on the LIVE dataset.
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paper_title: QUAD: quality assessment of documents

paper_content:

We propose a set of metrics that evaluate the ::: uniformity, sharpness, continuity, noise, stroke width variance,pulse width ratio, transient pixels density, entropy and variance of components to quantify the quality of a document image. The measures are intended to be used in any optical character recognition (OCR) engine to a priori estimate the expected performance of the OCR. The suggested measures have been evaluated on many document images, which have different scripts. The quality of a document image is manually annotated by users to create a ground truth. The idea is to correlate the values of the measures with the user annotated data. If the measure calculated matches the annotated description,then the metric is accepted; else it is rejected. In the set of ::: metrics proposed, some of them are accepted and the rest are ::: rejected. We have defined metrics that are easily estimatable. The metrics proposed in this paper are based on the feedback of homely grown OCR engines for Indic (Tamil and Kannada) languages. The metrics are independent of the scripts, and depend only on the quality and age of the paper and the printing. Experiments and results for each proposed metric are discussed. Actual recognition of the printed text is not performed to evaluate the proposed metrics. Sometimes, a document image containing broken characters results in good document image as per the evaluated metrics, which is part of the unsolved challenges. The proposed measures work on gray scale document images and fail to provide reliable information on binarized document image.
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paper_title: Learning features for predicting OCR accuracy

paper_content:

In this paper, we present a new method for assessing the quality of degraded document images using unsupervised feature learning. The goal is to build a computational model to automatically predict OCR accuracy of a degraded document image without a reference image. Current approaches for this problem typically rely on hand-crafted features whose design is based on heuristic rules that may not be generalizable. In contrast, we explore an unsupervised feature learning framework to learn effective and efficient features for predicting OCR accuracy. Our experimental results, on a set of historic newspaper images, show that the proposed method outperforms a baseline method which combines features from previous works.
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paper_title: A Method for Selecting Constrained Hand-Printed Character Shapes for Machine Recognition

paper_content:

Since handwritten characters vary in shape and writing-stroke sequence, it is desirable to develop a standard set of characters that are of high quality, so that not only are they easy to write, but they are also most suitable for machine recognition. A database of more than 100 000 alphanumeric patterns was assembled. It consisted of 174 models of the alphanumeric characters written by both left-handed and right-handed subjects. Based on frequency density and distance measurements, a nietric called the dispersion factor was computed to rank the various models. The principle of the metric is discussed, and results are given indicating the high quality models of the alphanumerics.
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paper_title: Character-Based Automated Human Perception Quality Assessment in Document Images

paper_content:

Large degradations in document images impede their readability and deteriorate the performance of automated document processing systems. Document image quality (IQ) metrics have been defined through optical character recognition (OCR) accuracy. Such metrics, however, do not always correlate with human perception of IQ. When enhancing document images with the goal of improving readability, e.g., in historical documents where OCR performance is low and/or where it is necessary to preserve the original context, it is important to understand human perception of quality. The goal of this paper is to design a system that enables the learning and estimation of human perception of document IQ. Such a metric can be used to compare existing document enhancement methods and guide automated document enhancement. Moreover, the proposed methodology is designed as a general framework that can be applied in a wide range of applications.
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paper_title: Image quality and readability

paper_content:

Determining the readability of documents is an important task. Human readability pertains to the scenario when a document image is ultimately presented to a human to read. Machine readability pertains to the scenario when the document is subjected to an OCR process. In either case, poor image quality might render a document unreadable. A document image which is human readable is often not machine readable. It is often advisable to filter out documents of poor image quality before sending them to either machine or human for reading. This paper is about the design of such a filter. We describe various factors which affect document image quality and the accuracy of predicting the extent of human and machine readability possible using metrics based on document image quality.
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paper_title: Quality assessment and restoration of typewritten document images

paper_content:

We present a useful method for assessing the quality of a typewritten document image and automatically selecting an optimal restoration method based on that assessment. We use five quality measures that assess the severity of background speckle, touching characters, and broken characters. A linear classifier uses these measures to select a restoration method. On a 139-document corpus, our methodology reduced the corpus OCR character error rate from 20.27% to 12.60%.
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paper_title: Prediction of OCR accuracy using simple image features

paper_content:

A classifier for predicting the character accuracy achieved by any Optical Character Recognition (OCR) system on a given page is presented. This classifier is based on measuring the amount of white speckle, the amount of character fragments, and overall size information in the page. No output from the OCR system is used. The given page is classified as either "good" quality (i.e. high OCR accuracy expected) or "poor" (i.e. low OCR accuracy expected). Results of processing 639 pages show a recognition rate of approximately 85%. This performance compares favorably with the ideal-case performance of a prediction method based upon the number of reject-markers in OCR generated text.
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paper_title: Automatic quality measurement of gray-scale handwriting based on extended average entropy

paper_content:

With a surge of interest in OCR in the 1990s, a large number of handwriting or handprinting databases have been built one after another around the world. One problem that researches encounter today is that all the databases differ in various ways including the script qualities. The paper proposes a method for measuring handwriting qualities that can be used for comparison of databases and objective test for character recognizers. The key idea involved is classifying character samples into a number of groups each characterizing a set of qualities. In order to evaluate the proposed method, we carried out experiments on the KU-1 database.
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paper_title: Automated image quality assessment for camera-captured OCR

paper_content:

Camera-captured optical character recognition (OCR) is a challenging area because of artifacts introduced during image acquisition with consumer-domain hand-held and Smart phone cameras. Critical information is lost if the user does not get immediate feedback on whether the acquired image meets the quality requirements for OCR. To avoid such information loss, we propose a novel automated image quality assessment method that predicts the degree of degradation on OCR. Unlike other image quality assessment algorithms which only deal with blurring, the proposed method quantifies image quality degradation across several artifacts and accurately predicts the impact on OCR error rate. We present evaluation results on a set of machine-printed document images which have been captured using digital cameras with different degradations.
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paper_title: Quality of OCR for degraded text images

paper_content:

Commercial OCR packages work best with high-quality scanned images. They often produce poor results when the image is degraded, either because the original itself was poor quality, or because of excessive photocopying. The ability to predict the word failure rate of OCR from a statistical analysis of the image can help in making decisions in the trade-off between the success rate of OCR and the cost of human correction of errors. This paper describes an investigation of OCR of degraded text images using a standard OCR engine (Adobe Capture). The documents were selected from those in the archive at Los Alamos National Laboratory. By introducing noise in a controlled manner into perfect documents, we show how the quality of OCR can be predicted from the nature of the noise. The preliminary results show that a simple noise model can give good prediction of the number of OCR errors.
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paper_title: Automatic filter selection using image quality assessment

paper_content:

We present a method for automatically selecting the best filter to treat poor quality printed documents using image quality assessment. We introduce five quality measures to obtain information about the quality of the images, and morphological filters to improve their quality. A training set of 370 images was used to develop the system. Experimental results on the test set show a significant improvement in the recognition rate from 73.24% using no filter at all to 93.09% after applying a filter that was automatically selected.
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paper_title: Handwriting Quality Evaluation

paper_content:

This paper presents an approach to evaluate the quality of handwritten letters based on the set of features that are used by human handwriting experts. The use of these attributes allows very intuitive interpretation of the results and as a consequence provides solid foundation for feedback to the end user of the system. A combination of an artificial neural network and an expert system is used to evaluate and grade each handwritten letter, as well as to provide feedback to the student. The application of such a system would be in the educational field for handwriting teaching and repair.
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paper_title: Quality assessment and restoration of typewritten document images

paper_content:

We present a useful method for assessing the quality of a typewritten document image and automatically selecting an optimal restoration method based on that assessment. We use five quality measures that assess the severity of background speckle, touching characters, and broken characters. A linear classifier uses these measures to select a restoration method. On a 139-document corpus, our methodology reduced the corpus OCR character error rate from 20.27% to 12.60%.
---
paper_title: Prediction of OCR accuracy using simple image features

paper_content:

A classifier for predicting the character accuracy achieved by any Optical Character Recognition (OCR) system on a given page is presented. This classifier is based on measuring the amount of white speckle, the amount of character fragments, and overall size information in the page. No output from the OCR system is used. The given page is classified as either "good" quality (i.e. high OCR accuracy expected) or "poor" (i.e. low OCR accuracy expected). Results of processing 639 pages show a recognition rate of approximately 85%. This performance compares favorably with the ideal-case performance of a prediction method based upon the number of reject-markers in OCR generated text.
---
paper_title: Automatic filter selection using image quality assessment

paper_content:

We present a method for automatically selecting the best filter to treat poor quality printed documents using image quality assessment. We introduce five quality measures to obtain information about the quality of the images, and morphological filters to improve their quality. A training set of 370 images was used to develop the system. Experimental results on the test set show a significant improvement in the recognition rate from 73.24% using no filter at all to 93.09% after applying a filter that was automatically selected.
---
paper_title: QUAD: quality assessment of documents

paper_content:

We propose a set of metrics that evaluate the ::: uniformity, sharpness, continuity, noise, stroke width variance,pulse width ratio, transient pixels density, entropy and variance of components to quantify the quality of a document image. The measures are intended to be used in any optical character recognition (OCR) engine to a priori estimate the expected performance of the OCR. The suggested measures have been evaluated on many document images, which have different scripts. The quality of a document image is manually annotated by users to create a ground truth. The idea is to correlate the values of the measures with the user annotated data. If the measure calculated matches the annotated description,then the metric is accepted; else it is rejected. In the set of ::: metrics proposed, some of them are accepted and the rest are ::: rejected. We have defined metrics that are easily estimatable. The metrics proposed in this paper are based on the feedback of homely grown OCR engines for Indic (Tamil and Kannada) languages. The metrics are independent of the scripts, and depend only on the quality and age of the paper and the printing. Experiments and results for each proposed metric are discussed. Actual recognition of the printed text is not performed to evaluate the proposed metrics. Sometimes, a document image containing broken characters results in good document image as per the evaluated metrics, which is part of the unsolved challenges. The proposed measures work on gray scale document images and fail to provide reliable information on binarized document image.
---
paper_title: Learning features for predicting OCR accuracy

paper_content:

In this paper, we present a new method for assessing the quality of degraded document images using unsupervised feature learning. The goal is to build a computational model to automatically predict OCR accuracy of a degraded document image without a reference image. Current approaches for this problem typically rely on hand-crafted features whose design is based on heuristic rules that may not be generalizable. In contrast, we explore an unsupervised feature learning framework to learn effective and efficient features for predicting OCR accuracy. Our experimental results, on a set of historic newspaper images, show that the proposed method outperforms a baseline method which combines features from previous works.
---
paper_title: Automated image quality assessment for camera-captured OCR

paper_content:

Camera-captured optical character recognition (OCR) is a challenging area because of artifacts introduced during image acquisition with consumer-domain hand-held and Smart phone cameras. Critical information is lost if the user does not get immediate feedback on whether the acquired image meets the quality requirements for OCR. To avoid such information loss, we propose a novel automated image quality assessment method that predicts the degree of degradation on OCR. Unlike other image quality assessment algorithms which only deal with blurring, the proposed method quantifies image quality degradation across several artifacts and accurately predicts the impact on OCR error rate. We present evaluation results on a set of machine-printed document images which have been captured using digital cameras with different degradations.
---
paper_title: Sorting qualities of handwritten Chinese characters for setting up a research database

paper_content:

Setting up a database containing a large volume of character samples is important for research on character recognition. As the character samples are collected, the samples must be classified into the correct category, it is also necessary to establish an index for measuring the quality of the samples so that one can give information about their quality when certain samples are mentioned. An automatic sorting procedure for Chinese characters is proposed based on the Bayes rule. Three features about Chinese characters are used for the sorting. The sorting results are checked by examining curves of the recognition rates for all samples as the samples are arranged with or without sorting. The experimental results show that the sorting method is effective. >
---
paper_title: Sharpness estimation for document and scene images

paper_content:

Images of document pages have different characteristics than images of natural scenes, and so the sharpness measures developed for natural scene images do not necessarily extend to document images primarily composed of text. We present an efficient and simple method for effectively estimating the sharp-ness/blurriness of document images that also performs well on natural scenes. Our method can be used to predict the sharpness in scenarios where images are blurred due to camera-motion (or hand-shake), defocus, or inherent properties of the imaging system. The proposed method outperforms the perceptually-based, no-reference sharpness work of [1] and [4], which was shown to perform better than 14 other no-reference sharpness measures on the LIVE dataset.
---
paper_title: Character-Based Automated Human Perception Quality Assessment in Document Images

paper_content:

Large degradations in document images impede their readability and deteriorate the performance of automated document processing systems. Document image quality (IQ) metrics have been defined through optical character recognition (OCR) accuracy. Such metrics, however, do not always correlate with human perception of IQ. When enhancing document images with the goal of improving readability, e.g., in historical documents where OCR performance is low and/or where it is necessary to preserve the original context, it is important to understand human perception of quality. The goal of this paper is to design a system that enables the learning and estimation of human perception of document IQ. Such a metric can be used to compare existing document enhancement methods and guide automated document enhancement. Moreover, the proposed methodology is designed as a general framework that can be applied in a wide range of applications.
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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 Method for Selecting Constrained Hand-Printed Character Shapes for Machine Recognition

paper_content:

Since handwritten characters vary in shape and writing-stroke sequence, it is desirable to develop a standard set of characters that are of high quality, so that not only are they easy to write, but they are also most suitable for machine recognition. A database of more than 100 000 alphanumeric patterns was assembled. It consisted of 174 models of the alphanumeric characters written by both left-handed and right-handed subjects. Based on frequency density and distance measurements, a nietric called the dispersion factor was computed to rank the various models. The principle of the metric is discussed, and results are given indicating the high quality models of the alphanumerics.
---
paper_title: Automatic quality measurement of gray-scale handwriting based on extended average entropy

paper_content:

With a surge of interest in OCR in the 1990s, a large number of handwriting or handprinting databases have been built one after another around the world. One problem that researches encounter today is that all the databases differ in various ways including the script qualities. The paper proposes a method for measuring handwriting qualities that can be used for comparison of databases and objective test for character recognizers. The key idea involved is classifying character samples into a number of groups each characterizing a set of qualities. In order to evaluate the proposed method, we carried out experiments on the KU-1 database.
---
paper_title: Human Image Preference and Document Degradation Models

paper_content:

Because most degraded documents are created by people, the preferences individuals have in relation to degraded documents are quite important. Their preferences may determine whether or not the documents they created are appropriate for machines. The goal of this study was to find relationships between preference and several parameters of a scanner degradation model. It was found that the difference in binarization threshold and the difference in edge displacement caused by the degradation both had strong linear relationships to preference. The width of the point spread function did not show such a relationship. These relationships were counterintuitive because degraded characters with thicker stroke widths than the original were preferred to those that had stroke widths closer to the original character.
---
paper_title: Sharpness estimation for document and scene images

paper_content:

Images of document pages have different characteristics than images of natural scenes, and so the sharpness measures developed for natural scene images do not necessarily extend to document images primarily composed of text. We present an efficient and simple method for effectively estimating the sharp-ness/blurriness of document images that also performs well on natural scenes. Our method can be used to predict the sharpness in scenarios where images are blurred due to camera-motion (or hand-shake), defocus, or inherent properties of the imaging system. The proposed method outperforms the perceptually-based, no-reference sharpness work of [1] and [4], which was shown to perform better than 14 other no-reference sharpness measures on the LIVE dataset.
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paper_title: QUAD: quality assessment of documents

paper_content:

We propose a set of metrics that evaluate the ::: uniformity, sharpness, continuity, noise, stroke width variance,pulse width ratio, transient pixels density, entropy and variance of components to quantify the quality of a document image. The measures are intended to be used in any optical character recognition (OCR) engine to a priori estimate the expected performance of the OCR. The suggested measures have been evaluated on many document images, which have different scripts. The quality of a document image is manually annotated by users to create a ground truth. The idea is to correlate the values of the measures with the user annotated data. If the measure calculated matches the annotated description,then the metric is accepted; else it is rejected. In the set of ::: metrics proposed, some of them are accepted and the rest are ::: rejected. We have defined metrics that are easily estimatable. The metrics proposed in this paper are based on the feedback of homely grown OCR engines for Indic (Tamil and Kannada) languages. The metrics are independent of the scripts, and depend only on the quality and age of the paper and the printing. Experiments and results for each proposed metric are discussed. Actual recognition of the printed text is not performed to evaluate the proposed metrics. Sometimes, a document image containing broken characters results in good document image as per the evaluated metrics, which is part of the unsolved challenges. The proposed measures work on gray scale document images and fail to provide reliable information on binarized document image.
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paper_title: Character-Based Automated Human Perception Quality Assessment in Document Images

paper_content:

Large degradations in document images impede their readability and deteriorate the performance of automated document processing systems. Document image quality (IQ) metrics have been defined through optical character recognition (OCR) accuracy. Such metrics, however, do not always correlate with human perception of IQ. When enhancing document images with the goal of improving readability, e.g., in historical documents where OCR performance is low and/or where it is necessary to preserve the original context, it is important to understand human perception of quality. The goal of this paper is to design a system that enables the learning and estimation of human perception of document IQ. Such a metric can be used to compare existing document enhancement methods and guide automated document enhancement. Moreover, the proposed methodology is designed as a general framework that can be applied in a wide range of applications.
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Title: Document Image Quality Assessment: A Brief Survey

Section 1: INTRODUCTION
Description 1: This section provides an overview of the importance and challenges of Document Image Quality Assessment (DIQA) and contrasts it with natural scene Image Quality Assessment (IQA).

Section 2: DOCUMENT IMAGE DEGRADATIONS
Description 2: This section details the processes and stages at which document images can become degraded, including creation, external degradation, digitization, and processing.

Section 3: DOCUMENT IMAGE DEGRADATION MODELING
Description 3: This section reviews the various models for simulating document image degradations and discusses the issues of parametrization, randomization, validation, and parameter estimation.

Section 4: OBJECTIVE DOCUMENT IMAGE QUALITY ASSESSMENT
Description 4: This section discusses methods for predicting document image quality using automatic, computational models, specifically focusing on OCR accuracy prediction and human perception.

Section 5: SUBJECTIVE DOCUMENT IMAGE QUALITY ASSESSMENT
Description 5: This section reviews methods for assessing document image quality based on human perception, including rating-based and pair-wise comparison approaches.

Section 6: SUMMARY
Description 6: This section summarizes the key points discussed in the survey, highlighting the importance and the challenges of DIQA.