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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Fakir Mohamed Fakir
Faculty of Sciences and Technics
Computer Science & IT Engineering

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Indexing Of Three Dimensions Objects Using GIST, Zernike & PCA Descriptors Driss Naji; Fakir Mohamed Fakir; O. Bencharef; B. Bouikhalene; A. Razouk
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 1: March 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (316.153 KB)

Abstract

In this paper, we present a new approach to object to recognition based on the combination of Zernike moments, descriptors Gist and PCA pair wise applied to color images. The recognition of objects are based on two approaches of classification the first use neural networks (NN) for learning stage and gratitude as well to the Support Vector Machines (SVM). The experimental results showed that the recognition by SVM is better than NN. We illustrate the proposed method on color images, including objects from the database COIL-100.DOI: http://dx.doi.org/10.11591/ij-ai.v2i1.825
Segmentation and Recognition of Arabic Printed Script Fakir Mohamed Fakir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 2, No 1: March 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (387.43 KB)

Abstract

In this work we present a method for the recognition of Arabic printed script. The major problem of the automatic reading of cursive writing is a segmentation of script to isolate characters. The recognition process consists of four phases: Preprocessing, segmentation, feature extraction and the recognition.In the preprocessing, the image is scanned and smoothed. The correction of skew lines is done by using Hough transform . In the second phase, the text is segmented into lines, words or parts of words and each word into characters based on the principle of projection of the histogram. Features such as:  density, profile, Hu moments and histogram are used to classifier the characters based on the Neural network.DOI: http://dx.doi.org/10.11591/ij-ai.v2i1.1236 
Co-Authors A. Razouk B. Bouikhalene Driss Naji O. Bencharef