Ghayda Al-Talib

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Arabic language is characterized by extensive use of dots or secondary characters associated with main body or primary characters. More than half of the Arabic characters can only be distinguished by these secondary characters. Hence recognition of these characters has a vital importance in Arabic OCR. In printed text the problem is much easier than(More)
This paper, proposed a classification approach that utilizes the high recognition ability of Hidden Markov Models (HMM s) to perform high accuracy of classification by exploiting the spatial inter pixels dependencies ( i.e. the context ) as well as the spectral information. Applying unsupervised classification to remote sensing images can provide benefits(More)
A fuzzy expert system for selected Arabic sub-words recognition is presented in this paper. For each sub-word pattern, membership values are determined for a number of fuzzy sets defined on the features extracted from the pattern. These sub-words consist of two characters and are written cursively, so, the first step is to segment the sub-words into two(More)
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