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Data clustering is an important step which evolves in many pattern recognition problems and decision making applications. This step had gained great interest and several approaches were proposed to improve the clustering quality. In this context, we proposed a new ensemble clustering system based on the use of a dynamic fuzzy exponent within fuzzy C-Means clustering, an unsupervised feature selection...
Text detection in natural scenes holds great importance in the field of research and still remains a challenge because of size, various fonts, line orientation, different illumination conditions, weak character and complex background in image. The contribution of the proposed method is filtering out complex backgrounds by utilizing two masks filtering based on text confidence map in the first step...
Text detection from images in natural scene is one of the most active research areas. It still remains a challenge for researchers because of the complexity of the image in the wild specifically their background. The state of the text presents also different problems of localization such as size, font, color and orientation. This paper presents a new method based on the location of the concentration...
We present a development of a new approach for automated diagnosis, based on classification of Magnetic Resonance (MR) human brain images. 2D Wavelet Transform and Spatial Gray Level Dependence Matrix (DWT-SGLDM) is used for feature extraction. For feature selection Simulated Annealing (SA) is applied to reduce features size. The next step in our approach is Stratified K-fold Cross Validation to avoid...
This paper proposes an automatic text-independent online Arabic writer identification system. The main contribution of our system is to explore the utility of Beta-elliptic model in features extraction for online writer identification, due to the rich output of Beta-elliptic model in terms of graphical, kinematical and biometrical data. The efficiency of the considered features has been evaluated...
Nowadays, there are many challenges for the logistics industry mainly with the integration of E-commerce and new sources of data such as smartphones, sensors, GPS and other devices. Those new data sources generate daily a huge quantity of unstructured data, to deal with such complex data, the use of big data analytic tools becomes an obligation. In this context, many works have been done recently...
Text detection is a basic step in many computer vision applications including video Optical Character Recognition (OCR), video indexation, understanding video content, etc. Actually, several sources such as mobile devices, monitoring cameras and social networks are generating every day billions of videos with different formats and uncertainty. Such videos require new methods to apply text detection...
Text in image is an important source of information. In this article, we describe an approach for detection of text in the scene file. Our method classify pixels into text and non-text areas using neural network and wavelet transformation. It is divided into two steps: a step offline and online step. The experimental results show the performance of our algorithm.
In this report, we propose to give a review of the most used clustering methods in the literature. First, we give an introduction about clustering methods, how they work and their main challenges. Second, we present the clustering methods with some comparisons including mainly the classical partitioning clustering methods like well-known k-means algorithms, Gaussian Mixture Modals and their variants,...
In this paper we present a new approach of Arabic diacritics modeling. The developed algorithm represents a section of the features extraction module of an online Arabic handwriting recognition system based on explicit grapheme segmentation strategy. The algorithm consists in three stages: first the detection of diacritics using the dimensions and the positions of the isolated handwriting strokes...
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