Gender identification at the word level in a multi-script environment is challenging due to variations posed by free-style handwriting of individuals and geographical differences in writing styles. This paper presents a new approach, Multi-Orientation-Scale Gabor Response Fusion (MOSGF), for gender identification at the word level using handwritten text. Our method has two steps: (i) word segmentation from unconstrained lines and (ii) gender identification at the word level. In the first step, the method explores the number of zero crossing points and gradient information for word segmentation from handwritten text lines. In the second step, employs Gabor responses at different orientations and scales to detect fine details in female and male handwriting. For each Gabor response, the proposed model estimates the correlation between average templates of all Gabor responses and the individual Gabor response to extract global consistency in writing. To strengthen correlation features, the proposed method uses the Mahalanobis distance measure, which extracts local similarity. Further, the proposed approach fuses correlation coefficient and distance-based features in a novel way. The fused features are then fed to a Neural Network (NN) for gender identification. Experiments on our dataset, which comprises Roman (English), Chinese, Farsi (Persian), Arabic, and Indian scripts, and a benchmark dataset, namely, IAM which includes English text, KHATT which includes Arabic, and QUWI which includes both English and Arabic, show that the proposed system outperforms the existing methods in terms of word segmentation and gender identification.

Handwritten documents possess immense significance in domains such as law, history, and administration. However, they are vulnerable to forgery, which can undermine their credibility and reliability. This paper aims to establish a dependable technique for identifying altered text in handwritten document images, even in scenarios with high levels of noise and blur. Our study investigates 10 distinct categories of handwritten text that have been altered through various forgery operations. The suggested approach employs the deep neural architectures VGG16 and Resnet50 as feature extractors. The architecture comprises three parts: Feature extraction using individual models, a feature fusion layer, and a classification layer. Initially, we optimize the training process and feature extraction using VGG16 and ResNet50. The feature vectors obtained from both models are then fused together in the feature fusion layer and input into the classification layer for the classification task. Experiments are conducted on a custom-created dataset as well as benchmark datasets including ICPR FDC, IMEI Forged Number, and Kundu to demonstrate that the proposed method is superior to existing approaches.

Due to the proliferation of sports-related multimedia content on the WWW, effective visual search and retrieval present interesting research challenges. These are caused by poor image quality, a wide range of possible camera points of view, pose variations on the part of athletes engaged in playing a sport, deformations of text appearing on sports person’s clothing and uniforms in motion, occlusions caused by other objects, etc. To address these challenges, this paper presents a new method for detecting text on human bodies in sports images. Unlike most existing methods, which attempt to exploit locations of a player’s torso, face, and skin, we propose an end-to-end episodic learning approach that employs inductive learning criteria for detecting clothing regions in an image, which are, in turn, then used for text detection. Our method integrates a Residual Network (ResNet) and Pyramidal Pooling Module (PPM) for generating a spatial attention map. The Progressive Scalable Expansion Algorithm (PSE) is adapted for text detection from these regions. Experimental results on our own dataset as well as several benchmarks (like RBNR and MMM which contain images of runners in marathons, and Re-ID which is a person re-identification dataset) demonstrate that the proposed method outperforms existing methods in terms of precision and F1-score. We also present results for sports images chosen from natural scene text detection datasets such as CTW1500 and MS-COCO to show the proposed method is effective and reliable across a range of inputs.

As more and more office documents are captured, stored, and shared in digital format, and as image editing software are becoming increasingly more powerful, there is a growing concern about document authenticity. To prevent illicit activities, this paper presents a new method for detecting altered text in document images. The proposed method explores the relationship between positive and negative coefficients of DCT to extract the effect of distortions caused by tampering by fusing reconstructed images of respective positive and negative coefficients, which results in Positive-Negative DCT coefficients Fusion (PNDF). To take advantage of spatial information, we propose to fuse R, G, and B color channels of input images, which results in RGBF (RGB Fusion). Next, the same fusion operation is used for fusing PNDF and RGBF, which results in a fused image for the original input one. We compute a histogram to extract features from the fused image, which results in a feature vector. The feature vector is then fed to a deep neural network for classifying altered text images. The proposed method is tested on our own dataset and the standard datasets from the ICPR 2018 Fraud Contest, Altered Handwriting (AH), and faked IMEI number images. The results show that the proposed method is effective and the proposed method outperforms the existing methods irrespective of image type.

Text detection in low light natural scene images is challenging due to poor image quality and low contrast. Unlike most existing methods that focus on well-lit (normally daylight) images, the proposed method considers much darker natural scene images. For this task, our method first integrates spatial and frequency domain features through fusion to enhance fine details in the image. Next, we use Maximally Stable Extremal Regions (MSER) for detecting text candidates from the enhanced images. We then introduce Cloud of Line Distribution (COLD) features, which capture the distribution of pixels of text candidates in the polar domain. The extracted features are sent to a Convolution Neural Network (CNN) to correct the bounding boxes for arbitrarily oriented text lines by removing false positives. Experiments are conducted on a dataset of low light images to evaluate the proposed enhancement step. The results show our approach is more effective compared to existing methods in terms of standard quality measures, namely, BRISQE, NIQE and PIQE. In addition, experimental results on a variety of standard benchmark datasets, namely, ICDAR 2013, ICDAR 2015, SVT, Total-Text, ICDAR 2017-MLT and CTW1500, show that the proposed approach not only produces better results for low light images, at the same time it is also competitive for daylight images.

Recently developed sophisticated image processing techniques and tools have made easier the creation of high-quality forgeries of handwritten documents including financial and property records. To detect such forgeries of handwritten documents, this paper presents a new method by exploring the combination of Chebyshev-Harmonic-Fourier-Moments (CHFM) and deep Convolutional Neural Networks (D-CNNs). Unlike existing methods work based on abrupt changes due to distortion created by forgery operation, the proposed method works based on inconsistencies and irregular changes created by forgery operations. Inspired by the special properties of CHFM, such as its reconstruction ability by removing redundant information, the proposed method explores CHFM to obtain reconstructed images for the color components of the Original, Forged Noisy and Blurred classes. Motivated by the strong discriminative power of deep CNNs, for the reconstructed images of respective color components, the proposed method used deep CNNs for forged handwriting detection. Experimental results on our dataset and benchmark datasets (namely, ACPR 2019, ICPR 2018 FCD and IMEI datasets) show that the proposed method outperforms existing methods in terms of classification rate.

Methods developed for normal 2D text detection do not work well for text that is rendered using decorative, 3D effects, etc. This paper proposes a new method for classification of 2D and 3D natural scene text images so that an appropriate recognition method can be chosen accordingly based on the classification results for better performance. The proposed method explores local gradient differences for obtaining candidate pixels, which represent a stroke. To study the spatial distribution of candidate pixels, we propose a measure, called COLD, which is denser for pixels toward the center of strokes and scattered for non-stroke pixels. This observation leads us to introduce mass features for extracting the regular spatial pattern of COLD, which indicates a 2D text image. The extracted features are fed into a Neural Network (NN) for classification. The proposed method is tested on (i) a new dataset introduced in this work (ii) a second dataset assembled from standard natural scene datasets (iii) Non-Text Image datasets which does not contain text, rather it contains objects. Experimental results of the proposed method on images with text and non-text show that the proposed method is independent of text. The proposed approach improves text detection and recognition performance significantly after classification.

Rapid advances in artificial intelligence have made it possible to produce forgeries good enough to fool an average user. As a result, there is growing interest in developing robust methods to counter such forgeries. This study presents a new Fourier spectrum‐based method for detecting forged text in video images. The authors' premise is that brightness distribution and the spectrum shape exhibit irregular patterns (inconsistencies) for forged text, while appearing more regular for original text. The method divides the spectrum of an input image into sectors and tracks to highlight these effects. Specifically, positive and negative coefficients for sectors and tracks are extracted to quantify the brightness distribution. Variations in the shape of the spectrum are analysed by determining the angular relationship between the principal axes and the sectors/tracks of the spectrum. Next, it combines these two features to detect forged text in the images of IMEI (International Mobile Equipment Identity) numbers and document. For evaluation, the following datasets are used: own video dataset and standard datasets, namely, IMEI number, ICPR 2018 Fraud Document Contest, and a natural scene text dataset. Experimental results show that the proposed method outperforms existing methods in terms of average classification rate and F ‐score.

Traditionally, page images undergo pre-processing before the later stages of document analysis are applied. One common pre-processing step is to calculate and correct for the presence of simple page skew through a compensating rotation. Such operations modify the original input image, however, and in doing so may discard or obscure useful information. In this paper, we examine the impact of page deskewing on the task of writer identification for complicated handwritten documents. As an alternative to rotating the page image, we demonstrate a method that compensates for page skew during feature extraction. Experimental evaluation involving 61 Arabic writers and 610 page images show that handling page skew during feature extraction can benefit writer ID with a significant 1.4% gain in accuracy. In addition, we also obtain a 4.7% gain after improving an existing contour-based feature extraction method.

The Document Analysis and Exploitation platform is a sophisticated technical environment that consists of a repository containing document images, implementations of document analysis algorithms, and the results of these algorithms when applied to data in the repository. The use of a web services model makes it possible to set up document analysis pipelines that form the basis for reproducible protocols. Since the platform keeps track of all intermediate results, it becomes an information resource for the analysis of experimental data. This paper provides a tutorial on how to get started using the platform. It covers the technical details needed to overcome the initial hurdles and have a productive experience with DAE.

We propose to adapt Turing's seminal 1950 test for machine intelligence to evaluating progress in document analysis systems. Our premise is that a problem can be considered solved if automated and human solutions to the underlying task are indistinguishable to a skeptical human judge. For the domain-specific problems of concern here, we reformulate the test to keep the interaction between judges and human/machine participants to graphical user interfaces that do not require natural language processing, a notable difference from Turing's original formulation. Examples of tasks that may lend themselves to such tests include detecting or identifying specific document components such as logos, photographs, tables, as well as writer and language identification. The administration of the test would be facilitated by commercial crowd-sourcing systems such as Amazon Mechanical Turk, as well as research platforms such as the Lehigh Document Analysis Engine (DAE) that accept arbitrary documents for input, record test results, and provide for trusted execution of submitted programs.

Tabular structure detection and recognition can be a valuable step in the analysis of unstructured documents. The noisy handwritten documents we try to analyze may contain pre-printed rulings as the substrate, hand-drawn rulings, machine-printed text, handwritten text, and signatures, in addition to the tabular structures which we wish to decompose into basic cells, rows, and columns. Although work has been done to machine-printed documents, noisy handwritten documents may require modified and/or new techniques. In this work, we try to detect and decompose tabular structures into 2-D grids of table cells simultaneously. First, we detect "key points" that help determine the physical and logical structure of tables. Then, we make use of the 2-D grid assumption to build grids of key points. Finally, we extract structural features for the Min-Cut/Max-Flow algorithm to recognize tabular structures. Experiments on 22 tables which contain 584 table cells show a cell precision of 100% and a cell recall of 93.3%.

In this paper, we propose a way to combine a pass- word with a speech biometric crypto system. We present two schemes to enhance verification performance in a biometric cryptosystem using password. Both can resist a pass- word brute-force search if biometrics are not compromised. Even if the biometrics are compromised, attackers have to spend many more attempts in searching for cryptographic keys when we compare ours with a traditional password- based approach. In addition, the experimental results show that the verification performance is significantly improved.

Table detection can be a valuable step in the analysis of unstructured documents. Although much work has been conducted in the domain of machine-print including books, scientific papers, etc., little has been done to address the case of handwritten inputs. In this paper, we study table detection in scanned handwritten documents subject to challenging artifacts and noise. First, we separate text components (machine-print, handwriting) from the rest of the page using an SVM classifier. We then employ a correlation-based approach to measure the coherence between adjacent text lines which may be part of the same table, solving the resulting page decomposition problem using dynamic programming. A report of preliminary results from ongoing experiments concludes the paper.

Ruling lines are commonly used to help people write neatly on paper. In document image analysis, however, they create challenges for handwriting recognition and writer identification. In this paper, we model ruling line detection as a multi-line linear regression problem and then derive a globally optimal solution giving the Least Square Error. We demonstrate the efficacy of the technique on both synthetic and real datasets. A comparative study shows that our algorithm outperforms a previously published method on the public Germana dataset.

In this paper, we present a fully operational, scalable and open architecture allowing end-to-end document analysis benchmarking without needing to develop the whole pipeline. By decomposing the analysis process into coarse-grained tasks, and by building upon community provided state-of-the art algorithms, our architecture allows any combination of elementary document analysis algorithms, regardless their running system environment, programming language or data structures. Its flexible structure makes it straightforward to plug in new algorithms, compare them to other algorithms, and observe the effects on end-to-end tasks without need to install, compile or otherwise interact with any other software than one's own.

This contest aims to provide a metric giving indications on the influence of individual document analysis stages to overall end-to-end applications. Contestants are provided with a full, working pipeline which operates on a page image to extract useful information. The pipeline is built with clearly identified analysis stages (e.g. binarization, skew detection, layout analysis, OCR) that have a formalized input and output. Contestants are invited to contribute their own algorithms as an alternative to one or more of the initially provided stages. The evaluation measures the overall impact of the contributed algorithm on the final (end-of-pipeline) output.

Open problems are defined differently in document image analysis than in the physical sciences, theoretical computer science, or mathematics. Instead of a formal definition, problems in DIA are stated in terms of automation of an application area (e.g., postal address reading) or a scientific sub field (e.g., image compression). The notion of a successful solution may be based on (1) the relative accuracy of automated vs. expert solutions (given specific data and degree of manual tuning), (2) the distinguish ability of automated output from human output (a Turing Test), (3) the degree of current community interest (via conferences and journals), and/or (4) economic considerations. Because of the lack of formal definition for DIA problems, heuristics predominate over provably correct algorithms, and full disclosure of implementation details as well as populations and samples is essential. Results on available test sets are often only tangentially related to motivating applications. In addition, interest in automating certain tasks has been evolving rapidly as a result of advances in technology. Further community discussion of these issues may accelerate progress and symbiosis with allied disciplines.

Vote counting accuracy has become a well-known issue in the vote collection process. Digital image processing techniques can be incorporated in the analysis of printed election ballots. Current image processing techniques in the vote collection process are heavily dependent on the anticipated, geometric positioning of the vote. These techniques don't account for markings made outside of the requested field of input. Using various form dropout techniques, however, every mark on the form can be extracted and used by the machine to make an intelligent decision. Most methods will still miss a few marks and result in a few false alarms. This paper explores methods of voting between the results of the different mark extraction methods to improve recognition. To provide diversity a simple image subtraction technique is paired with a distance transform and a morphology based algorithm. The result has a higher detection rate and a lower false alarm rate.

Resources are presented for fostering paper-based election technology. They comprise a diverse collection of real and simulated ballot and survey images, and software tools for ballot synthesis, registration, segmentation, and ground truthing. The grids underlying the designated location of voter marks are extracted from 13,315 degraded ballot images. The actual skew angles of sample ballots, recorded as part of complete ballot descriptions compiled with the interactive ground-truthing tool, are compared with their automatically extracted parameters. The average error is 0.1 degrees. These results provide a baseline for the application of digital image analysis to the scrutiny of electoral ballots.

No abstract available.

Optical scan voting is considered by many to be the most trustworthy option for conducting elections because it provides an independently verifiable record of each voter’s intent. While op-scan technology has been in use for decades, attempts to improve the machine reading of ballots raises a range of interesting issues in document image analysis. Work thus far has been hindered by a lack of real-world data, since ballots associated with actual elections are kept secure from the public and normally destroyed after a period time. Fortunately, as a result of a recent challenged election in the State of Minnesota, a large collection of op-scan ballot images was made available for public inspection on the World Wide Web. In this paper, we present this unique resource to the document analysis community. We also describe our efforts to annotate the collection, including the latest version of a graphical tool we have developed for collecting ground-truth interpretations, along with the protocol now being employed. The collection, consisting of ballot images, file formats, and associated truth data, is being made openly available to facilitate research in this important area.

Paper often includes pre-printed ruling lines to help people write more neatly. This particular example of real- world noise can have a serious impact on applications such as handwriting recognition and writer identification, however. In this work, we investigate the effects of ruling lines on writer ID. We study a method for detecting and removing ruling lines and test its utility for Arabic writer identification through a series of experiments. Our preliminary results show that under realistic assumptions where ruling lines are expected to have different properties across the collection, e.g., thickness, spacing, etc., removing them significantly improves identification performance. We conclude with a discussion of work-in-progress to examine follow up questions raised by our initial investigations.

In this paper we present a procedure for removing ruling lines from a handwritten document image that does not break existing characters. We take advantage of common ruling line properties such as uniform width, predictable spacing, position vs. text, etc. The proposed process has no effect on document images without ruling lines, hence no a priori discrimination is required. The system is evaluated on synthetic page images in five different languages.

Recent work has shown that certain handwriting biometrics are susceptible to forgery attacks, both human- and machine-based. In this paper, we examine a new scheme for using handwritten input that attempts to address such concerns. Pseudo-signatures are intended to be easy for users to create and reproduce while being resilient to forgeries. Here we evaluate their feasibility in terms of usability and security through several user studies. Our initial experiments suggest that, when well-chosen, pseudo-signatures may prove to be an attractive biometric,although more research is required.

Recent developments have resulted in dramatic changes in the way elections are conducted, both in the United States and around the world. Well-publicized flaws in the security of electronic voting systems have led to a push for the use of verifiable paper records in the election process. In this paper, we describe the application of document analysis techniques to facilitate the manual auditing of elections,both to assure the reliability of the final outcome as well as to help reconcile the differences that may arise between repeated scans of the same ballot. We show how techniques developed for document duplicate detection can be applied to this problem, and present experimental results that demonstrate the efficacy of our approach. Related issues concerning machine support for the auditing of elections are also discussed.

Portable ballot counters using camera technology and manual paper feed are potentially more reliable and less expensive than scanner based systems. We show that the spatial sampling rate, geometric linearity, point spread function, and photometric transfer function of off-the-shelf consumer cameras are acceptable for ballot imaging. However, scanner illumination is much more uniform than can be economically accomplished for variable size ballots. Therefore flat-field compensation must be designed into the image processing software. We illustrate the mechanical design of a prototype camera based ballot reader based on our comparative observations.

The push toward voting via hand marked paper ballots has focused attention on the limitations of current optical scan systems. Discrepancies between human and machine interpretations of ballot markings can lead to a loss of trust in the election process. In this paper, a style-based approach to ballot recognition is proposed in which marks are recognized collectively rather than in isolation. The consistency of a voter's style is leveraged to improve the overall accuracy of the system. We compare style-based recognition to various kinds of singlet classifiers and show that it outperforms them by a substantial margin.

Multi-observer segmentation evaluation is useful in the imaging community. We have developed web-based software for automatic performance evaluation of multiple image segmentations which is based on the Baysian decision framework. It computes a probabilistic estimate of the true segmentation (ground truth map) and performance measures for the individual segmentations (sensitivity and specificity). The strength of the tool is that it integrates the two kinds of prior knowledge of segmentations: the truth prior (the prior probability) and the observer prior (the performance measures of observers), which can generate more accurate evaluations.

As a result of well-publicized security concerns with direct recording electronic (DRE) voting, there is a growing call for systems that employ some form of paper artifact to provide a verifiable physical record of a voter's choices. In this paper, we present a system we are developing to support a multi-institution, cross-disciplinary research project examining issues that arise when paper ballots are used in elections. We survey the motivating factors behind our work, discuss the special constraints raised in processing ballots as opposed to more general document images, and describe the current status of our system.

Optical mark sensing, i.e., detecting whether a “bubble” has been filled in, may seem straightforward. However, on US election ballots the shape, intensity, size and position of the marks, while specified, are highly variable due to a diverse electorate. The ballots may be produced and scanned by poorly maintained equipment. Yet near-perfect results are required. To improve the current technology, which has been subject to criticism, components of a process for identifying marks on an optical sense ballot are evaluated. When marked synthetic ballots are compared to an unmarked ballot, the absolute difference of adaptive thresholded images gives best detection rates for all darknesses of marks, but at a false alarm rate increase. Simple absolute differencing can give good detection results with lower false alarm rates.

Biometric security is a topic of rapidly growing importance in the areas of user authentication and cryptographic key generation. In this paper, we describe our steps toward developing evaluation methodologies for behavioral biometrics that take into account threat models that have been largely ignored. We argue that the pervasive assumption that forgers are minimally motivated (or, even worse, naive) is too optimistic and even dangerous. Taking handwriting as a case in point, we show through a series of experiments that some users are significantly better forgers than others, that such forgers can be trained in a relatively straightforward fashion to pose an even greater threat, that certain users are easy targets for forgers, and that most humans are a relatively poor judge of handwriting authenticity, and hence, their unaided instincts cannot be trusted. Additionally, to overcome current labor-intensive hurdles in performing more accurate assessments of system security, we present a generative attack model based on concatenative synthesis that can provide a rapid indication of the security afforded by the system. We show that our generative attacks match or exceed the effectiveness of forgeries rendered by the skilled humans we have encountered.

This paper presents an overview of three problem areas where ASAP computing might be profitably applied. Drawn from the fields of pattern recognition, bioinformatics, and biometric security, sufficient detail is provided to explain these selections and the role that ASAP might play, as well as to encourage those so inclined to take a closer look.

"Hackers" have written malicious programs to exploit online services intended for human users. As a result, service providers need a method to tell whether a web site is being accessed by a human or a machine. We expect a parallel scenario as spoken language interfaces become common.\nIn this paper, we describe a Reverse Turing Test (i.e., an algorithm that can distinguish between humans and computers) using speech. We present a test that depends on the fact that human recognition of distorted speech is far more robust than automatic speech recognition techniques.\nOur analysis of 18 different sets of distortions demonstrates that there are a variety of ways to make the problem hard for machines. In addition, humans and speech recognition systems make different kinds of mistakes, and this difference can be employed to improve discrimination.

The principle that for every document analysis task there exists a mechanism for creating well-defined ground-truth is a widely held tenet. Past experience with standard datasets providing ground-truth for character recognition and page segmentation tasks supports this belief. In the process of attempting to evaluate several table recognition algorithms we have been developing, however, we have uncovered a number of serious hurdles connected with the ground-truthing of tables. This problem may, in fact, be much more difficult than it appears. We present a detailed analysis of why table ground-truthing is so hard, including the notions that there may exist more than one acceptable "truth" and/or incomplete or partial "truths".

While techniques for evaluating the performance of lower-level document analysis tasks such as optical character recognition have gained acceptance in the field, attempts to formalize the problem for higher-level algorithms that incorporate more complex structure have been less successful. We describe an intuitive, easy-to-implement scheme for the problem of performance evaluation when document recognition results are represented in the form of a directed acyclic graph. We present results from two simulation studies based on different graph models and one experiment using a well known page segmentation algorithm to demonstrate the applicability of the approach.

This paper introduces a framework for clarifying and formalizing the duplicate document detection problem. Four distinct models are presented, each with a corresponding algorithm for its solution derived from the realm of approximate string matching. The robustness of these techniques is demonstrated through a set of experiments using data reflecting real-world degradation effects.

In this paper, we discuss the notion of treating electronic ink as first class data without attempting to recognize it by presenting two different variations of approximate ink matching (AIM) for searching ink data. We also illustrate a pen-based electronic document annotating and browsing system and methods for searching handdrawn personal notes employing the described matching schemes. Adapting from the Learning by Knowledge paradigm, we propose a semantic matching network that applies semantics of Chinese language early in the process of ink matching. Finally we evaluate several key components in our entire ink matching network via experiments. Preliminary experimental results show the approximate ink matching algorithms perform well, despite the informal and highly variable nature of Chinese handwriting. Our experiments also show some promising results on semantic matching and the feasibility of our semantic matching architecture.

The bitmap obtained by scanning a printed pattern depends on the exact location of the scanning grid relative to the pattern. We consider ideal sampling with a regular lattice of delta functions. The displacement of the lattice relative to the pattern is random and obeys a uniform probability density function defined over a unit cell of the lattice. Random-phase sampling affects the edge-pixels of sampled patterns. The resulting number of distinct bitmaps and their relative frequencies can be predicted from a mapping of the original pattern boundary to the unit cell (called a module-grid diagram). The theory is supported by both simulated and experimental results. The module-grid diagram may be useful in helping to understand the effects of edge-pixel variation on optical character recognition.

The authors examine the problem of locating and extracting text from images on the World Wide Web. They describe a text detection algorithm which is based on color clustering and connected component analysis. The algorithm first quantizes the color space of the input image into a number of color classes using a parameter-free clustering procedure. It then identifies text-like connected components in each color class based on their shapes. Finally, a post-processing procedure aligns text-like components into text lines. Experimental results suggest this approach is promising despite the challenging nature of the input data.

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.

In this paper we examine the effects of random-phase spatial sampling on the optical character recognition process. We start by presenting a detailed analysis in the case of 1-dimensional patterns. Empirical data demonstrate that our model is accurate. We then give experimental results for more complex, 2-dimensional patterns (i.e. printed, scanned characters). Spatial sampling seems to account for a significant amount of the variability seen in practice.

A general-purpose approach for enhancing the accuracy of optical character recognition is described. By taking the view that the printed page is a data transmission channel, the authors raise the possibility of error detecting/correcting codes designed specifically for the OCR process. They present experimental results that demonstrate the feasibility of fully automated, 100% accurate OCR for computer typeset documents.>

The authors propose representing uncertain parameters as intervals and present a theoretical framework based on interval algebra for manipulating these ranges. To illustrate this methodology, they modify an existing RC analysis algorithm (Crystal's PR-Slope model) to create one which computes worst-case delay bounds when given uncertain input parameters. They provide proofs of correctness for the approach and test its performance. Two alternate interval-based techniques which produce even tighter bounds than the original approach are also presented. When compared to Monte Carlo simulation, the interval methods are more precise and significantly faster.>

The authors introduce input/output (I/O) overhead psi as a complexity measure for VLSI implementations of two-dimensional lattice computations of the type arising in the simulation of physical systems. It is shown by pebbling arguments that psi = Omega (n/sup -1/) when there are n/sup 2/ processing elements available. If the results must be observed at every generation and if no on-chip storage is allowed, the lower bound is the constant 2. The authors then examine four VLSI architectures and show that one of them, the multigeneration sweep architecture also has I/O overhead proportional to n/sup -1/. A closed-form for the discrete minimization equation giving the optimal number of generations to compute for the multigeneration sweep architecture is proved.>

Many objective functions used to evaluate placement quality contain uncertain parameters (e.g. channel width, wire length). While these values can be estimated, they cannot be precisely known until the layout is finished. As a result, current automatic placement algorithms use 'expected' values in their objective functions and return one possible estimate of placement quality. An algorithm that uses the full range of potential values when computing placement cost can yield a more credible prediction of placement quality and reveal more about the structure of optimal configurations. An interval-based approach to modeling uncertainty in automatic placement is proposed. It is used to illustrate the authors' methods by implementing an interval branch and bound placement algorithm.>

A method is presented for modeling the effects of uncertainty in RC analysis. Representing uncertain parameters as intervals, interval algebra was used to create a rigorous framework for manipulating these uncertain values. The authors then modified an existing RC analysis algorithm (Crystal's PR-slope model) to illustrate their approach. Although Crystal was chosen for the experiments, the same techniques may be applied to other timing analysis algorithms. Compared to Monte Carlo simulation, the interval algorithm is much more efficient, operating several thousand times faster for the same quality results. For stages with few transistors, the accuracy of both methods is similar: both come within 10% of the true values.>