nils-herrmann/clean_scirepeval huggingface dataset:
LBP-based degraded document image binarization

Most of the classical threshold-based methods for document image binarization use simple features carried out from the spatial pixels values of the document images. In this paper, we present a new binarization method for degraded documents, based on Local Binary Pattern (LBP) as a texture measure. The mean and variance of pixels are computed respectively from both the original document image and the LBP image. Then, these features are used within a threshold-based method. Another variant is computed by combining a contrast information with the LBP operator to overcome the drawback caused by the poor contrasted document images. Experimental results conducted on DIBCO datasets and compared against some state-of-the-art methods, prove the effective use of the LBP for binarizing historical documents.
Visual language processing (VLP) of ancient manuscripts: Converting collections to windows on the past

Ancient manuscripts constitute a primary carrier of cultural heritage globally, and they are currently being intensively digitized all over the world to ensure their preservation, and, ultimately, the wide accessibility of their content. Critical to this research process are the legibility of the documents in image form, and access to live texts. Several state-of-the-art methods and approaches have been proposed and developed to address the challenges associated with processing these manuscripts. However, there is a huge amount of data involved, and also the high cost and scarcity of human expert feedback and reference data call for the development of fundamental approaches that encompass all these aspects in an objective and tractable manner. In this paper, we propose one such approach, which is a novel framework for the computational pattern analysis of ancient manuscripts that is data-driven, multilevel, self-sustaining, and learning-based, and takes advantage of the large quantities of unprocessed data available. Unlike many approaches, which fast-forward to the processing and analysis of feature vectors, our innovative framework represents a new perspective on the task, which starts from ground zero of the problem, which is the definition of objects. In addition, it leverages the data-driven mining of relations among objects to discover hidden but persistent links between them. The problem is addressed at three main levels. At the lowest level, that of images, it tackles automatic, data-driven enhancement and restoration of document images using spatial, spectral, sparse, and graph-based representations of visual objects. At the second level, which is transliteration, directed graphical models, HMMs, Undirected Random Fields, and spatial relations models are used to extract the live text of manuscript images, which reduces dependency on human experts. Finally, at the highest level, that of network analysis of the relations among objects (from patches and words to manuscripts and writers) involves the search for `social networks' linking manuscripts. Considering this approach under the umbrella of Visual Language Processing (VLP), we hope that it will be further enriched by the research community, in the form of new insights and approaches contributed at the various levels.
A framework for a GMM-UBM based speaker verification and the need of a large arabic database

This paper presents a framework for the Gaussian mixture models-Universal Background Model (GMM-UBM) system, which has proved to be an effective probabilistic model for speaker verification, and has been widely used in most of state-of-the-art systems. In this work we focus on different feature extraction techniques, and different client model training strategies. An experimental evaluation of this framework is done on the TIMIT database. Finally, we explain the need of a large Arabic database in order to estimate the appropriate universal background models UBMpsilas required for the optimal performance of this kind of parameterization.
Nonrigid 2D/3D Registration of Coronary Artery Models With Live Fluoroscopy for Guidance of Cardiac Interventions

A 2D/3D nonrigid registration method is proposed that brings a 3D centerline model of the coronary arteries into correspondence with bi-plane fluoroscopic angiograms. The registered model is overlaid on top of interventional angiograms to provide surgical assistance during image-guided chronic total occlusion procedures, thereby reducing the uncertainty inherent in 2D interventional images. The proposed methodology is divided into two parts: global structural alignment and local nonrigid registration. In both cases, vessel centerlines are automatically extracted from the 2D fluoroscopic images, and serve as the basis for the alignment and registration algorithms. In the first part, an energy minimization method is used to estimate a global affine transformation that aligns the centerline with the angiograms. The performance of nine general purpose optimizers has been assessed for this problem, and detailed results are presented. In the second part, a fully nonrigid registration method is proposed and used to compensate for any local shape discrepancy. This method is based on a variational framework, and uses a simultaneous matching and reconstruction process to compute a nonrigid registration. With a typical run time of less than 3 s, the algorithms are fast enough for interactive applications. Experiments on five different subjects are presented and show promising results.
A modified version of a formal pruning algorithm based on local relative variance analysis

A modified version of a formal pruning algorithm initially proposed by Englebercht [November, 2001] using variance analysis of sensitivity is presented. We propose a new modification of the algorithm by applying the pruning procedure on each layer starting from the output layer to the input layer. Contrarily, to the work of Englebercht where the pruning is performed on the entire net that we denote in this paper global pruning, we shall prune layer by layer with the use of a pruning decision based on a local parameter variance nullity coefficient (LPVN). These coefficients are then classified in an ordered list which allows the decision making examples showing that in some cases we can reach about 30% improvement in terms of coefficients and neurons removal in order to get the best neural network pruned. A comparison study is given on some real world learning and generalization.
A comparative study of fast neural network learning algorithms

In this paper, a comparative study of different neural network fast training procedures is presented. A decade of progress in speeding the feedforward neural network learning algorithms is reviewed. Different algorithms and techniques in unified form are presented and discussed with particular emphasis on their corresponding behaviour including the reduction of the number of iteration, their complexity, their accuracy and other parameters. Issues for the choice of faster algorithms are covered. An overview of further up to date new techniques is also discussed. Simulation results on a couple of benchmarks namely a 4-bit parity checker and the circle in the square problem are given to compare the proposed fast algorithms.
Shape recognition on a Riemannian manifold

In this paper, we propose to perform shape recognition on a Riemannian manifold. Shape representation on a manifold have the advantage to be intrinsically invariant to shape preserving transformation, such as scaling and translation. Also, shape distance can be naturally computed because Riemannian manifolds are metric spaces. We propose to use the square-root velocity manifold (SRV), which model the shape external contour as a unit-length curve. We detail a dynamic programming algorithm for curve alignment w.r.t. parameterization, which respects the unit-length constraint. Then, we increase the robustness of the SRV representation to shape deformations with additional features. In order to be resilient to occlusion, the distance between two curves is performed in two steps. First the curves are aligned and the less matching parts are removed; then the resulting curves are aligned and the distance is evaluated. Finally, a support vector machine classifier is trained based on the pairwise shape distance for a robust recognition. Promising results are obtained using state-of-the-art benchmarks.
Text extraction from degraded document images

In this work, a robust segmentation method for text extraction from the historical document images is presented. The method is based on Markovian-Bayesian clustering on local graphs on both pixel and regional scales. It consists of three steps. In the first step, an over-segmented map of the input image is created. The resulting map provides a rich and accurate semi-mosaic fragments. The map is processed in the second step, similar and adjoining sub-regions are merged together to form accurate text shapes. The output of the second step, which contains accurate shapes, is processed in the final step in which, using clustering with fixed number of classes, the segmentation will be obtained. The method employs significantly the local and spatial correlation and coherence on both the image and between the stroke parts, and therefore is very robust with respect to the degradation. The resulting segmented text is smooth, and weak connections and loops are preserved thanks to robust nature of the method. The output can be used in succeeding skeletonization processes which require preservation of the text topology for achieving high performance. The method is tested on real degraded document images with promising results.
A Software-Defined Scalable and Autonomous Architecture for Multi-tenancy

Scalability for distributed Data Center Networks (DCNs) has long been a goal of the network research and industrial community. To support dynamically increasing demands from multi-tenants, the network providers have to duplicate or share virtual resources for satisfying tenants' requests. However, current Software-Defined Networking (SDN) architectures have major drawbacks including lack of scalability and cross Virtual Tenant Network (VTN) communication. They rely only on the flexibility of control plane and neglect management plane important role. SDN scalability bottleneck affects directly the network/VTN scalability. In front of the fast growing network, it is widely accepted that the network of the future will require more capabilities such as self-awareness, self-control and self-management. At the core of these challenges is providing elastic isolation for multi-tenancy and involving tenant in management and control to reach the scalability objective and reduce the complexity of management operations of large DCNs. To address these challenges, the Open virtual Network Management and Security (Open vNMS) is proposed for supporting transparent multi-tenancy while both network and VTN scalability is solved. Basing on elastic L2 isolation using SDN components' flexibility, we design an autonomic architecture to provide self-control, self-management and self-adaptive capabilities for the network. The experiment results showed that the proposed design offers negligible overhead and guarantees the network performance.
Welcome message from Technical Program Chairs

We are very pleased to present the technical program we have prepared for this 13th edition of ICDAR. We are so pleased to have received over 400 submissions (488 summaries submitted and 401 complete submissions in the end). The papers were submitted from 46 countries across the 5 continents: Africa (6 countries / 50 papers), Americas (4 countries / 35 papers), Asia (20 countries / 173 papers), Australia (1 country / 3 papers), and Europe (15 countries/140 papers).
Segmentation-based document image denoising

In this work, a robust method of document images denoising is presented. The simple idea is combining the NLM filter and a Markovian segmentation into regions. The NLM method filtering allows participation of far, but proper pixels in the denoising process. Although the weights of non-similar (irrelevant) pixels are very small, high number of these pixels results in introduction of blur. In this work we present a new method to select the best candidate pixels based on their similarity. Before performing denoising process, we segment the noisy image into regions where similar pixels belong to a same homogeneous region r. Thus, to denoise a given pixel i, which belong to a region ri, the proposed algorithm looks for the neighbor pixels of i and includes only those belonging to same region ri. This method is tested on real noisy document images with promising results and it presents an improvement comparing to the original NLM.
Fast training of multilayer perceptrons with a mixed norm algorithm

A new fast training algorithm for the multilayer perceptron (MLP) is proposed. This new algorithm is based on the optimization of a mixed least square (LS) and a least fourth (LF) criterion producing a modified form of the standard back propagation algorithm (SBP). To determine the updating rules in the hidden layers, an analogous back propagation strategy used in the conventional learning algorithms is developed. This permits the application of the learning procedure to all the layers. Experimental results on benchmark applications and a real medical problem are obtained which indicates significant reduction in the total number of iterations, the convergence time, and the generalization capacity when compared to those of the SBP algorithm.
Gabor Filters for Degraded Document Image Binarization

Most of the classical methods for degraded document binarization are based on the pixel gray level intensity or on simple pixel neighborhood information such as mean or variance to compute the binarization threshold. Moreover, these information are extracted from the spatial domain of the document image which are not very discriminative. In this paper, we propose to estimate texture information based on Gabor filters for ancient degraded documents. First, the dominant slant angle of the document image script is computed by using the Fourier transform. Then, this dominant angle is used within a weighted sum of angles in a Gabor filter bank in order to capture more efficiently the document image foreground (text). This information, combined with the variance and the mean extracted respectively from spatial and frequency domains are used for estimating the binarization threshold. Three variants are used for evaluating the performance of Gabor filter bank, which are based on Niblack's, Sauvola's, and Wolf's thresholds. Experimental results conducted on DIBCO Datasets show that the proposed method is more appropriate for poor contrasted documents and ink-bleed through degradations.
Personalizable Pen-Based Interface Using Lifelong Learning

In this paper, we present a new method to design customizable self-evolving fuzzy rule-based classifiers. The presented approach combines an incremental clustering algorithm with a fuzzy adaptation method in order to learn and maintain the model. We use this method to build an evolving handwritten gesture recognition system, that can be integrated into an application to provide personalization capabilities. Experiments on an on-line gesture database were performed by considering various user personalization scenarios. The experiments show that the proposed evolving gesture recognition system continuously adapts and evolve according to new data of learned classes, and remains robust when introducing new unseen classes, at any moment during the lifelong learning process.
Cloud Computing: A Risk Assessment Model

Cloud computing has recently emerged compelling paradigm by introducing several characteristics such as on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. Despite the fact that cloud computing offers huge cost benefits for companies, the unique security challenges have been introduced in a cloud environment that make risk assessment challenging. Cloud consumers need a protection to their cloud applications against cyber attacks. Although some security controls and policies are devised for each element of cloud computing, we need a framework with overall quantitative risk assessment model. The aim of this paper is to propose a framework for assessing the security risks associated with cloud computing platforms. The fully quantitative, iterative, and incremental approach enables cloud customer/provider to assess and manage cloud security risks. A proper result of risk assessment leads to have appropriate risk management mechanism for mitigating risks and reach to an acceptance security level.
An agent-based model to evaluate smart homes sustainability potential

Information and communication technologies are believed to contribute to reduce environmental impacts in other economic sectors. Smart homes aim to this purpose through improved resource management, but actual environmental benefits are uncertain. Indeed, an increase in efficiency may lead to an increase in consumption and thus in environmental impacts, a phenomenon called rebound effect. Here we describe an agent-based approach which simulates human behaviors during the use phase of smart homes and allows computation of environmental impacts including those due to rebound effect. In our simulation, smart homes reduce environmental impact by 2%. However, rebound dims between 6% to 24% of the smart homes' environmental benefits depending on impact category. Moreover, a sensitivity analysis indicates that weather and electricity mix variability should be considered when designing policies related to smart homes technology. Life cycle assessment by this method accounts for user behaviors, which is necessary when studying some complex systems found in sustainable consumption. Evaluate the environmental performance of a smart-city and study different rebound mechanisms are possible applications of the developed approach.
Dynamic observations and dynamic state termination for off-line handwritten word recognition using HMM

HMM has been successfully used to model 1D data, e.g. voice signals. Their use to model 2D patterns was not as successful due to a major difficulty, in describing the 2D data using 1D observation sequences. In this paper, we discuss the importance of this issue and present an improved method to extract 1D observations from the dynamics of off-line handwritten words. The method is based on pen trajectory estimation techniques. The paper also includes description of our HMM classifier which allows dynamic termination states to achieve enhanced discriminative power. Experimental results show the applicability and usefulness of the proposed method. As a result of using the termination probability in HMM modeling, the top 1/sup st/ recognition rate increased by 10%.
New Formulation of SVM for Model Selection

Model selection for support vector machines concerns the tuning of SVM hyperparameters as C controlling the amount of overlap and the kernel parameters. Several criteria developed for tuning the SVM hyperparameters, may not be differentiable w.r.t. C, consequently, gradient-based optimization methods are not applicable. In this paper, we propose a new formulation for SVM that makes possible to include the hyperparameter C in the definition of the kernel parameters. Then, tuning hyperparameters for SVM is equivalent to choosing the best values of kernel parameters. We tested this new formulation for model selection by using the criterion of empirical error, technique based on generalization error minimization through a validation set. The experiments on different benchmarks show promising results confirming our approach.
A traffic visualization framework for monitoring large-scale inter-datacenter network

Diversity, dynamicity, and the huge volume of traffic in the network between datacenters has risen network administrators concerns on how to efficiently visualize their system in real-time. To deal with these challenges, we present in this paper a visualization framework based on advanced machine learning, traffic characterization, sampling, and graphical visualization algorithms, which aims to efficiently support inter-datacenter network monitoring. Experimental results show the framework is able to process real-time big flows and provides human-friendly interactive graphical representations.
Query-by-example word spotting using multiscale features and classification in the space of representation differences

Word spotting in document images is a challenging problem, due to the large intra-class variability in handwritten shapes and the lack of labeled data. To tackle these challenges, this paper proposes an efficient multiscale representation for word images, which is learned in an unsupervised manner using the spherical k-means algorithm. A pooling function is applied in a spatial grid to obtain a fixed-length vector of features, robust to small shifts in the image. Scale variability in handwritten data is also considered by using patches of various sizes in the encoding process. Another important contribution of this work is to model the training-based word spotting task as a classification problem in the space of representation differences, thereby allowing the learned model to find matches for word classes that were not seen in training. The proposed system is evaluated on the well-known George Washington (GW) dataset. Experimental results show that our system outperforms state-of-the-art word spotting approaches in both training-free and training-based scenarios.
Multi-Objective Optimization in Dynamic Content Adaptation of Slide Documents

In mobile web conferencing, slide decks should be optimized before delivery to meet the constraints and environments of target mobile devices. To deliver optimally adapted slides, a trade-off between the visual aspect and delivery time must be reached. Static adaptation methods are CPU-intensive, and require large storage space. The dynamic approach is attractive as the optimal version is created on the fly when the actual slide is to be shared. Existing dynamic solutions are optimized for the resolution of the target mobile device and use good visual quality settings. However, they do not control the resulting data size, which creates serious usability issues, such as increasing the delivery time. Prediction-based methods require much less memory and processing resources than static approaches while yielding an excellent user experience. In this paper, we propose a multi-objective dynamic content adaptation framework, in which we maximize the visual quality and minimize the delivery time simultaneously. We compare our solution with an ideal optimal point, called utopia, and with all the optimal solutions (Pareto front) provided by a static exhaustive system. The obtained results show that our framework yields solutions very close to the utopia and, for the majority of the documents tested, the obtained solutions are on the Pareto front.