Nadir Farah

Classifiers combination and syntax analysis for Arabic literal amount recognition

Automatic handwriting recognition has a variety of applications in real world problems, such as mail sorting and check processing. Recently, it has been demonstrated that combining the decisions of several classifiers and integrating multiple information sources can lead to better recognition results. This article presents an approach for recognizing handwritten Arabic literal (legal) amounts. The proposed system uses a set of holistic structural features to describe the words. These features are presented to three classifiers: multilayer neural network, k nearest neighbor, and fuzzy k nearest neighbor. The classification results are then combined using several schemes; we retained the score summation one for this work. A syntactic post-classification process is then carried out to find the best match among the candidate words. The performance of this approach is superior to the system which ignores all contextual information and simply relies on the recognition scores of the recognizers.

Arabic Words Recognition with Classifiers Combination: An Application to Literal Amounts

The recognition of handwritten bank check literal amount is a problem that humans can solve easily. As a problem in automatic machine reading and interpreting, it presents a challenge and an interesting field of research. An approach for recognizing the legal amount for handwritten Arabic bank check is described in this article. The solution uses multiple information sources to recognize words. The recognition step is preformed in a parallel combination schema using holistic word structural features. The classification stage results are first normalized, and the combination schema is performed, after which using contextual information, the final decision on the candidate words can be done. Using this approach obtained results are more interesting than those obtained with individual classifiers.

Optimized Tumor Breast Cancer Classification Using Combining Random Subspace and Static Classifiers Selection Paradigms

Breast cancer is the most frequently diagnosed cancer in women worldwide and the leading cause of cancer death among females. Currently the most effective method for early detection and screening of breast abnormalities is mammography. Computer aided design (CAD) systems are used to assist radiologists in better classification of tumor in a mammography as benign or malignant. Ensemble classifier construction has received considerable attention in the recent years. In the modeling of classifier ensemble, many researchers believe that the success of classifier ensembles only when classifier members present diversity among themselves. The most widely used ensemble creation techniques are focused on incorporating the concept of diversity with the construction of different features subsets or selection of the most diverse components from initial classifiers pool. Therefore the motivation of this work is to propose a CAD system using a novel classification approach based on feature selection and static classifier selection schemes.

Using diversity in classifier set selection for arabic handwritten recognition

The first observation concerning Arabian manuscript reveals the complexity of the task, especially for the used classifiers ensemble. One of the most important steps in the design of a multi-classifier system (MCS), is the its components choice (classifiers). This step is very important to the overall MCS performance since the combination of a set of identical classifiers will not outperform the individual members. To select the best classifier set from a pool of classifiers, the classifier diversity is the most important property to be considered. The aim of this paper is to study Arabic handwriting recognition using MCS optimization based on diversity measures. The first approach selects the best classifier subset from large classifiers set taking into account different diversity measures. The second one chooses among the classifier set the one with the best performance and adds it to the selected classifiers subset. The performance in our approach is calculated using three diversity measures based on correlation between errors. On two database sets using 9 different classifiers, we then test the effect of using the criterion to be optimized (diversity measures,), and fusion methods (voting, weighted voting and Behavior Knowledge Space). The experimental results presented are encouraging and open other perspectives in the classifiers selection field especially speaking for Arabic Handwritten word recognition.

3D face recognition using covariance based descriptors

Abstract In this paper, we propose a new 3D face recognition method based on covariance descriptors. Unlike feature-based vectors, covariance-based descriptors enable the fusion and the encoding of different types of features and modalities into a compact representation. The covariance descriptors are symmetric positive definite matrices which can be viewed as an inner product on the tangent space of ( S y m d + ) the manifold of Symmetric Positive Definite (SPD) matrices. In this article, we study geodesic distances on the S y m d + manifold and use them as metrics for 3D face matching and recognition. We evaluate the performance of the proposed method on the FRGCv2 and the GAVAB databases and demonstrate its superiority compared to other state of the art methods.

Rule Based Neural Networks Construction for Handwritten Arabic City-Names Recognition

A recent innovation in artificial intelligence research has been the integration of multiple techniques into hybrid systems. These systems seek to overcome the deficiencies of traditional artificial techniques by combining techniques with complementary capabilities. At the crossroads of symbolic and neural processing, researchers have been actively investigating the synergies that might be obtained from combining the strengths of these two paradigms. In this article, we deal with a knowledge based artificial neural network for handwritten Arabic city-names recognition. We start with words perceptual features analysis in order to construct a hierarchical knowledge base reflecting words description. A translation algorithm then converts the symbolic representation into a neural network, which is empirically trained to overcome the handwriting variability.

From static to dynamic ensemble of classifiers selection: Application to Arabic handwritten recognition

Arabic handwriting word recognition is a challenging problem due to Arabics connected letter forms, consonantal diacritics and rich morphology. One way to improve the recognition rates classification task is to improve the accuracy of individual classifiers; another, is to apply ensemble of classifiers methods. To select the best classifier set from a pool of classifiers, the classifier diversity is considered one of the most important properties in static classifier selection. However, the advantage of dynamic ensemble selection versus static classifier selection is that used classifier set depends critically on the test pattern. In this paper, we propose two approaches for Arabic handwriting recognition AHR based on static and dynamic ensembles of classifiers selection. The first one selects statically the best set of classifiers from a pool of classifier already designed based on diversity measures. The second one represents a new algorithm based on Dynamic Ensemble of Classifiers Selection using Local Reliability measure DECS-LR. It chooses the most confident ensemble of classifiers to label each test sample dynamically. Such a level of confidence is measured by calculating the proposed local reliability measure using confusion matrixes constructed during training level. We show experimentally that both approaches provide encouraging results with the second one leading to a better recognition rate for AHR system using IFN_ENIT database.

Arabic word recognition by classifiers and context

Given the number and variety of methods used for handwriting recognition, it has been shown that there is no single method that can be called the “best”. In recent years, the combination of different classifiers and the use of contextual information have become major areas of interest in improving recognition results. This paper addresses a case study on the combination of multiple classifiers and the integration of syntactic level information for the recognition of handwritten Arabic literal amounts. To the best of our knowledge, this is the first time either of these methods has been applied to Arabic word recognition. Using three individual classifiers with high level global features, we performed word recognition experiments. A parallel combination method was tested for all possible configuration cases of the three chosen classifiers. A syntactic analyzer makes a final decision on the candidate words generated by the best configuration scheme. The effectiveness of contextual knowledge integration in our application is confirmed by the obtained results.

A new hybrid method combining genetic algorithm and support vector machine classifier: Application to CAD system for mammogram images

Breast cancer continues to be one of the most common cancers, and survival rates critically depend on its detection in the initial stages. Several studies have demonstrated the benefits and potential of using CAD (Computer-Assisted Diagnosis) systems to help specialists in their clinical interpretation of mammograms. CAD is based essentially on 2 main steps: Extraction of pertinent features and classification. In fact, several types of features are used in this work characterizing the extracted masses which are: texture features based on co-occurrence matrix and shape features based on Hu moments and central moments. All these features represent feature vector used in training and testing the used classifier. To reduce dimensionality and optimize classification process a new approach based on genetic algorithm is proposed. It incorporates Svm classifier results as part of multi objective function for fitness function. Once the best subset of features is chosen, classification is made by SVM classifier using Gaussian kernel function. Experimental results demonstrate the effectiveness of the proposed algorithm.

Mid-long term Algerian electric load forecasting using regression approach

Electrical load is a major input factor in economic development. To support economic growth and meet the demands in the future, the load forecasting has become a very important task for electric power stations. Therefore, several techniques have been used to accomplish this task. In this study, our interest is focused on the multiple regression approach, especially, linear and exponential regression for medium-long term load forecasting. The choice of this approach is due to the lack of data does not allow us to use artificial intelligence approaches such as neural networks. In addition to the regression approach, we used a system of electric load profile that allows us to obtain the power has a smaller scale (hour, day, week) to get the peaks. Data that has been used in this work represent electric load consumption and were taken from the Algerian national electricity company.