nils-herrmann/clean_scirepeval huggingface dataset:
Coupling observation/letter for a Markovian modelisation applied to the recognition of Arabic handwriting

A perfect segmentation method would be capable to segment words in letters. It would be then possible to define a process on letters. Unfortunately, such a method is almost impossible to obtain due to the nature of handwritten words. To tackle this problem, our approach segments the word into graphemes. We propose in this paper an analytical approach based on the Hidden Markovian Models (HMMs) to manage the defaults of the segmentation module. We also selected an optimal alphabet of graphemes in order to increase the performances of the recognition system. Furthermore, HMMs being developed exploit and model the notion of sub-words that is inherent to Arabic handwriting. An average correction of recognition rate of over 82.5% is obtained (in the first rank) with a lexicon of 232 different Tunisian state names.
Score Fusion of SVD and DCT-RLDA for Face Recognition

Although information fusion in unimodal or multimodal biometric systems can be performed at various levels, integration of the matching score level is the most common approach. Starting from the fact; that the fusion will be efficient if and only if the fused approaches are complementary not fully competitive. We propose in this paper the fusion of two projection based face recognition algorithms: singular value decomposition (SVD) using the left and right singular vectors of the face image as a face feature stored in a matrix and regularized Linear Discriminant Analysis in DCT domain (DCT-RLDA) which is known by its computational efficiency in addition to discrimination power. Experiments conducted on the ORL database indicate that the application of the Min-Max, Z-score score normalization schemes followed by a simple fusion strategies (simple sum, weighted sum, append) confirm the benefits of the proposed approach in terms of identification rate and processing time.
A Variational Approach to Degraded Document Enhancement

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

Perceptual analysis is an interesting topic in the field of image processing, and can be considered a missing link between image processing and human vision. Of the various forms of perception, one of the most important and best known is shape perception. In this paper, a framework based on the online nonlocal patch means (NLPM) method is developed, which is designed to infer possible perceptual observations of an input image using the knowledge images provided. Thanks to the speed of online NLPM, the proposed method can simulate the transformation of the input image to the final perceptual image in real time. In order to improve the performance of the method, a hidden chain series is considered for the model that delivers faster convergence. The capability of the method is evaluated on several well-known perceptual examples.
CorrC2G: Color to Gray Conversion by Correlation

In this letter, a novel decolorization method is proposed to convert color images into grayscale. The proposed method, called CorrC2G, estimates the three global linear weighting parameters of the color to gray conversion by correlation. These parameters are estimated directly from the correlations between each channel of the RGB image and a contrast image. The proposed method works directly on the RGB channels; it does not use any edge information nor any optimization or training. The objective and subjective experimental results on three available benchmark datasets of color to gray conversion, e.g., Cadik, CSDD, and Color250, show that the proposed decolorization method is highly efficient and comparable to recent state-of-the-art decolorization methods. The MATLAB source code of the proposed method is available at: http://www.synchromedia.ca/system/files/CorrC2G.m.
Robust NL-Means Filter With Optimal Pixel-Wise Smoothing Parameter for Statistical Image Denoising

Most denoising methods require that some smoothing parameters be set manually to optimize their performance. Among these methods, a new filter based on nonlocal weighting (NL-means filter) has been shown to have a very attractive denoising capacity. In this paper, we propose fixing the smoothing parameter of this filter automatically. The smoothing parameter corresponds to the bandwidth h of a local constant regression. We use the Cp statistic embedded in Newton's method to optimize h in a point-wise fashion. This statistic also has the advantage of being a reliable measure of the quality of the denoising process for each pixel. In addition, we introduce a robust regression in the NL-means filter designed to greatly reduce the blur yielded by the weighting. Finally, we show how the automatic denoising model can be extended to images degraded by multiplicative noise. Experiments conducted on images with additive and multiplicative noise demonstrate a high denoising power with a degree of detail preservation...
An Empirical Evaluation of Supervised Dimensionality Reduction for Recognition

In the literature, many dimensionality reduction methods have been proposed and applied to recognition tasks, including handwritten digits recognition, character recognition and string recognition. However, it is usually difficult for the researchers to decide which method is the optimal choice for the problem at hand. In this paper, we empirically compare some supervised dimensionality reduction methods on handwritten digits recognition, English letter recognition and ancient Arabic sub word recognition, to evaluate their performance on the recognition tasks. These compared methods include traditional linear dimensionality reduction approach (linear discriminant analysis, LDA), locality-based manifold learning approach (marginal Fisher analysis, MFA) and relational learning approach (probabilistic relational principal component analysis, PRPCA). Experimental results and statistical tests show that locality-based manifold learning approach (MFA) generally performs well in terms of recognition accuracy, but with high computational complexity, traditional linear dimensionality reduction approach (LDA) is efficient, but not necessarily to deliver the best result, relational learning approach (PRPCA) is promising, and more efforts should be dedicated to this area.
Online handwritten gesture recognition based on Takagi-Sugeno fuzzy models

In this paper, we present a new method for incremental online handwritten gesture recognition based on fuzzy rules. This approach allows starting from a scratch with no previously learned classes and adding new ones lifelong. Unlike methods based on evolving mountain clustering, our approach suits incremental concept better. We introduce a new method for evolving clustering and usage of incremental density measurement for determining the membership function which significantly improves the results. Density measurement as membership function allows using only few parameters instead of the costly covariance matrices and does not require any estimating by averaging and thus preventing from information lost. We also introduce a new set of features based on a shape of gestures. Combination of these new system characteristics thus lowers memory and computational requirements while significantly increasing recognition rate.
Two-stage classification system combining model-based and discriminative approaches

For the tasks of classification, two types of patterns can generate problems: ambiguous patterns and outliers. Furthermore, it is possible to separate classification algorithms into two main categories. Discriminative approaches try to find better separation among all classes and minimize the first type of error. But, in general, they cannot deal with outliers. Besides, model-based approaches make outlier detection possible but are not sufficiently discriminative. Thus, we propose to combine a model-based approach with support vector classifiers (SVC) in a two-stage classification system. Another advantage of this combination is reducing the principal burden of SVC: the processing time necessary to make a decision. Finally, experiments on handwriting digit recognition have shown that it is possible to maintain the accuracy of SVCs, while decreasing complexity significantly.
Restoration and Segmentation of Highly Degraded Characters Using a Shape-Independent Level Set Approach and Multi-level Classifiers

Segmentation of ancient documents is challenging. In the worst cases, text characters become fragmented as the results of strong degradation processes. New active contour methods allow to handle difficult cases in a spatially coherent fashion. However, most of those method use a restrictive, a priori shape information that limit their application. In this work, we propose to address this issue by combining two complementary approaches. First, multi-level classifiers, which take advantage of the stroke width a priori information, allow to locate candidate character pixels. Second, a level set active contour scheme is used to identify the boundary of a character. Tests have been conducted on a set of ancient degraded Hebraic character images. Numerical results are promising.
Automatic reading of cursive scripts using human knowledge

Presents a model for reading cursive scripts which has an architecture inspired by a reading model and which is based on perceptual concepts. We limit the scope of our study to the off-line recognition of isolated cursive words. First of all, we justify why we chose McClelland & Rumelhart's (1981) reading model as the inspiration for our system. A brief resume/spl acute/ of the method's behavior is presented and the main originalities of our model are underlined. After this, we focus on the new updates added to the original system: a new baseline extraction module, a new feature extraction module and a new generation, validation and hypothesis insertion process. After implementation of our method, new results have been obtained on real images from a training set of 184 images, and a testing set of 100 images, and are discussed. We are concentrating now on validating the model using a larger database.
A Survey on Metrics and Measurement Tools for Sustainable Distributed Cloud Networks

Energy efficiency and emissions awareness are core capabilities for sustainable and lower cost distributed cloud networks. In this context, metrics are fundamental for comparison and management purposes, along with the methods and tools which support such metrics’ capture and analysis. However, prior works on green metrics and tools have presented only a partial view, mainly as a result of the recent advances in green networking technologies. In this survey, we present an extensive study of metrics, methods, and tools to support sustainable operations in distributed cloud networks, with the aim of providing an end-to-end and up-to-date scenario to support current and coming research, as well as to analyze existing gaps.
ARTIST: ART-2A Driven Generation of Fuzzy Rules for Online Handwritten Gesture Recognition

Incremental learning, especially when learning from a scratch, has a lot of interest for online gesture recognition. However the lack of learning examplers combined to low computational cost suggests building robust and efficient learning machines. In this paper we introduce a hybrid model of ART-2A neural network combined to Takagi-Sugeno (TS) neuro-fuzzy network. The latter model is applied for online handwritten gesture recognition, when the learning is starting from scratch and no class information, such as gesture type or number of classes, is predefined. Moreover, using ART-2A neural network and our novel distance measure, the computational complexity of the whole model decreases while preserving high accuracy. Furthermore, we exploit the forgetting dilemma of online learning by introducing a competitive Recursive Least Squares method for TS models. Together, all the modeling has shown promising results.
Building a perception based model for reading cursive script

This paper presents a new perception based model for reading cursive script. We describe the organization of our pseudo-neuronal system and show the role of activation mechanism in perceiving and reading cursive script. We have introduced into our model some characteristics specific to cursive script. First, we use more appropriate features such as ascenders and descenders. Second, we deal with the ambiguity of letter location by introducing the concept of the fuzzy position. The location as well as the missing letters are deduced from the context (i.e. the word-letter lexicon). After implementation of our method, preliminary qualitative results have been obtained and are discussed. We are concentrating now on further formalizing and generalizing the proposed model on a larger data base.
Financial document processing based on staff line and description language

Millions of financial transactions take place every day. Associated with them are documents such as bank cheques, payment slips and bills which have to be processed. A great deal of time, effort and money will be saved if they can be entered into the computer and processed automatically. According to the specific characteristics of financial documents, it can be concluded that it is possible to build a system for recognizing specific types of financial documents, instead of a complex and general one aiming at different kinds of documents. In this paper, a financial document recognition prototype system which can process bank cheques, payment slips and bills, is presented. It consists of four major parts: (a) document image acquisition including scanning and binarization, (b) fixed document processing subsystem based on the detection of staff lines, (c) flexible document processing subsystem operating in a form description language (FDL), and (d) character recognition. Numerous experimental results are presented and discussed.
Transparent Clouds: An Enhancement to Abstraction

With the introduction of various hardware/software technologies such as Cloud Technologies or Virtualization technologies, there has been a great potential to reuse ICT resources. These technologies also provide various advantages including resource consumption reduction (in the use phase only or in the whole life cycle). In addition, there are additional potentials regarding the resource opacity introduced by these technologies. These potentials could improve cloud solutions and enable tweakability of components' nonessential features that may be not captured in the component models. This is especially of great advantage in the case of heterogeneous/disaggregated infrastructure where developing comprehensive models to cover everything is practically impossible. In this work, by leveraging on the concept of pathways, we develop a few mechanisms that enable transparency and therefore tweakability of features even in the presence of abstraction and heterogeneity. In particular, the layered-stack approach to system decomposition is considered because of its association with software defined networking (SDN). For a concrete example, the case of dynamic frequency scaling of processors is considered and it is shown that the associated consumption could be considerably reduced without requiring additional changes to the middle-layer components.
Background region-based algorithm for the segmentation of connected digits

The authors propose a character segmentation algorithm which is a region-based approach using background pixels. Some interesting background regions are found automatically by performing two independent filtering steps: top-down filtering and bottom-up filtering. Relationships between the resulting background components are considered to select plausible regions favorable to segmentation. For preliminary experimentation, the authors collected 120 samples of connected digits written by 12 people, 97 pairs were successfully segmented.<<ETX>>
The green sustainable telco cloud: Minimizing greenhouse gas emissions of server load migrations between distributed data centres

Among the innovative approaches to reduce the greenhouse gas (GHG) emissions of data centres during their use phase, electrical power from renewable sources appears promising. However, renewable electricity is often intermittent due to meteorological conditions. Consequently, the regional availability of renewable power varies constantly over time. This created the opportunity to deploy cloud computing systems relying on data centres located in different regions. Cloud computing technology enables real-time load migration to a data centre in the region where the GHG emissions per kWh are the lowest. While this approach is becoming popular to manage distributed data centres, there is still room for improvement in its implementation. Indeed, the consequences of data centre power demand migrations across electric networks and the resulting GHG emissions are usually neglected. In this project, we developed a novel GHG emission factor based on the sources of electricity affected by the server load migrations. Then, we used this emission factor in a simulation of distributed data centres to minimize their GHG emissions. Results show, the use of the novel emission factor enables an extra reduction of 23% of GHG emissions as compared to the usual approach.
Monitoring and measurement system for green operation of geographically distributed ICT services

Despite recent efforts and important results already achieved, the reduction of energy consumption and carbon emissions by Information and Communication Technologies is still far from the expected goals. As the annual growth in traffic is doubling every two years with more and more connections to the Internet, to be energy and carbon-aware it is paramount to implement a Monitoring and Measurement System which supports green strategies in a geographically distributed environment. Such an environment has some specific challenges that must be taken into account, such as the WAN connection, security and latency concerns. On the other hand, it also provides opportunities to reduce operational costs and emissions, improve reliability and resources management etc. This work proposes a framework which is capable of supporting green metrics in network monitoring. The framework comprises temporally differentiated data on emission factors and provides ground information able to support different applications. We have implemented the framework in a nationwide testbed and our experiments show the framework is able to provide the ground information for customizable green metrics, like power/energy, traffic, and carbon equivalent emissions. This framework can be used as a support for a variety of applications which depend on energy and emissions metrics.
Semisupervised Least Squares Support Vector Machine

The least squares support vector machine (LS-SVM), like the SVM, is based on the margin-maximization performing structural risk and has excellent power of generalization. In this paper, we consider its use in semisupervised learning. We propose two algorithms to perform this task deduced from the transductive SVM idea. Algorithm 1 is based on combinatorial search guided by certain heuristics while Algorithm 2 iteratively builds the decision function by adding one unlabeled sample at the time. In term of complexity, Algorithm 1 is faster but Algorithm 2 yields a classifier with a better generalization capacity with only a few labeled data available. Our proposed algorithms are tested in several benchmarks and give encouraging results, confirming our approach.
Visual data extraction from bi-level document images using a generalized kernel family with compact support, in scale-space

Presents a generalization of a new kernel family with compact support in scale space which we recently published (Vision Interface, pp. 445-52, May 1999). We have shown that the proposed kernels are able to recover the information loss when using the Gaussian kernel, while they drastically reduce the processing time. Furthermore, the generalized kernel family preserves all the properties of the previous one and it offers other important properties, like the behavior of the first and second derivatives which are guaranteed to be the same as the Gaussian kernel one at any scale space. The latter property plays an important role in image processing, as we show in this paper. The construction and some properties shown in the previous version are recalled and some of the new properties of the new version are proven. An application of extracting handwritten data from noisy bi-level document images is presented to point out the practical impact of the improved version of the proposed kernels.
Information Retrieval Based on OCR Errors in Scanned Documents

An important proportion of documents are document images, i.e. scanned documents. For their retrieval, it is important to recognize their contents. Current technologies for optical character recognition (OCR) and document analysis do not handle such documents adequately because of the recognition errors. In this paper, we describe an approach that integrates the detection of errors in scanned texts without relying on a lexicon, and this detection is integrated in the research process. The proposed algorithm consists of two basic steps. In the first step, we apply editing operations on OCR words that generate a collection of error-grams and correction rules. The second step uses query terms, error-grams, and correction rules to create searchable keywords, identify appropriate matching terms, and determine the degree of relevance of retrieved document images. Algorithms has been tested on 979 document images provided by Media-team databases from Washington University, and the experimental results obtained show the effectiveness of our method and indicate improvement in comparison with the standard methods such as exact or partial matching, N-gram overlaps, and Q-gram distance.
A segmentation method for touching italic characters

Segmentation is an essential part of a recognition system. It is difficult to handle touching characters, especially for italic fonts. We present a method to achieve the accurate segmentation of touching italic characters. It is free of slant correction, so extra noises will not be introduced. We use slant projection and contour analysis to find the segmentation points. Then the shortest path approach is adopted for accurately locating the cut path of each candidate segmentation point. Based on dynamic programming, we can find the best segmentation result from those cut paths. By this method, we can improve the recognition rate on italic fonts.