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—The explosive growth of image data leads to the research and development of image content searching and indexing systems. Image annotation systems aim at annotating automatically animage with some controlled keywords that can be used for indexing and retrieval of images. This paper presents a comparative evaluation of the image content annotation system by(More)
The aim of this work is to present a system for recognizing isolated Arabic printed characters. This system goes through several stages: preprocessing, feature extraction and classification. Zernike moments, invariant moments and Walsh transformation are used to calculate the features. The classification is based on multilayer neural networks. A recognition(More)
Many features extraction method and classifiers are used singly, with modest results, for automatic image annotation. In order to improve the semantic image annotation accuracy, this document provides an automatic system to annotate image content by using a fusion of 3 classifier and a combination of some features extraction methods; multiclass support(More)
Most of the reported works in the field of character recognition systems achieve modest results by using a single method for calculating the parameters of the character image and a single approach in the classification phase of the system. So, in order to improve the recognition rate, this document proposes an automatic system to recognize isolated printed(More)
— In this article, we describe the recognition system of Amazigh handwritten letters. The SURF descriptor, specifically the SURF-36, and the GIST descriptor are used for extracting feature vectors of each letter from our database which consists of 25740 manuscripts isolated Amazigh characters. All the feature vectors of each letter form a training set which(More)
Due to the large amounts of multimedia data prevalent on the Web, researchers and industries are beginning to pay more attention to the Multimedia Semantic Web. Despite of decades of research, neither model based approaches can provide quality annotation to images. Many features extraction method and classifiers are used singly, with modest results, for(More)
This paper provides an approach to automatically recognize the Tifinagh characters. The proposed recognition system is based on Texture, Walsh transformation and GIST descriptors as feature extraction methods while the Bayesian Networks are used as a classifier. A comparative study between the Texture descriptor, Walsh transformation and GIST descriptor is(More)
To perform a semantic search on a large dataset of images, we need to be able to transform the visual content of images (colors, textures, shapes) into semantic information. This transformation, called image annotation, assigns a caption or keywords to the visual content in a digital image. In this paper we try to resolve partially the region homogeneity(More)
A large percentage of photos on the Internet cannot be reached by search engines because of the semantic gap due to the absence of textual meta-data. Despite of decades of research, neither model based approaches can provide quality annotation to images. Many segmentation algorithms use a low-level predicates to control the homogeneity of the regions. So,(More)
The Tifinagh alphabet-IRCAM is the official alphabet of the Amazigh language widely used in North Africa [1]. It includes thirty-one basic letter and two letters each composed of a base letter followed by the sign of labialization. Normalized only in 2003 (Unicode) [2], ICRAM-Tifinagh is a young character repertoire. Which needs more work on all levels. In(More)