This paper presents a new technique of high accuracy to recognize both typewritten and handwritten English and Arabic texts without thinning. After segmenting the text into lines (horizontal segmentation) and the lines into words, it separates the word into its letters. Separating a text line (row) into words and a word into letters is performed by using the region growing technique (implicit s...

This paper presents the results of classifying Arabic text documents using the N-gram frequency statistics technique employing a dissimilarity measure called the “Manhattan distance”, and Dice’s measure of similarity. The Dice measure was used for comparison purposes. Results show that N-gram text classification using the Dice measure outperforms classification using the Manhattan measure.

The goal of this paper is to present an overview about the thinning problem in Arabic text recognition. Thinning "Skeletonization" is a very crucial stage in the ACR, it simplifies the text shape and reduces the amount of data that needs to be handled and it is usually used as a pre-processing stage for recognition and storage systems. The skeleton of Arabic text can be used for each ...

In this study, a fuzzy similarity approach for Arabic web pages classification is presented. The approach uses a fuzzy term-category relation by manipulating membership degree for the training data and the degree value for a test web page. Six measures are used and compared in this study. These measures include: Einstein, Algebraic, Hamacher, MinMax, Special case fuzzy and Bounded Difference ap...

This paper focuses on Automatic Arabic classifications. Arabic language is highly inflectional and derivational language which makes text mining a complex task. In classifying Arabic text, there are many published experimental results. Since these results came from different datasets, authors and evaluation metrics, we cannot compare the performance of the experimented classifiers. In this pape...

Text classification (TC) is the process of classifying documents into a predefined set of categories based on their content. Arabic language is highly inflectional and derivational language which makes text mining a complex task. In this paper we applied the Support Vector Machines (SVM) model in classifying Arabic text documents. The results compared with the other traditional classifiers Baye...

In organizations, a large amount of information exists in text documents. Therefore, it is important to use text mining to discover knowledge from these unstructured data. Automatic text classification considered as one of important applications in text mining. It is the process of assigning a text document to one or more predefined categories based on their content. This paper focus on classif...