This paper presents a novel holistic technique for classifying Arabic handwritten text documents. The classification of Arabic handwritten documents is performed in several steps. First, the Arabic handwritten document images are segmented into words, and then each word is segmented into its connected parts. Second, several structural and statistical features are extracted from these connected ...

Developments in Arabic information retrieval did not follow the increasing use of the Arabic Web during the last decade. Semantic indexing in a language with high inflectional morphology, such as Arabic, is not a trivial task and requires a text analysis in the original language. Excepting cross-language retrieval methods or limited studies, the main efforts, for developing semantic analysis me...

Text classification is a supervised learning technique that uses labeled training data to derive a classification system (classifier) and then automatically classifies unlabelled text data using the derived classifier. In this paper, we investigate Naïve Bayesian method (NB) and Support Vector Machine algorithm (SVM) on different Arabic data sets. The bases of our comparison are the most popula...

In this paper we present Document analysis and classification system to segment and classify contents of Arabic document images. This system includes preprocessing, document segmentation, feature extraction and document classification. A document image is enhanced in the preprocessing by removing noise, binarization, and detecting and correcting image skew. In document segmentation, an algorith...

Text classification and text gradation are important for language teachers. Profiling readability studies examine textual linguistic features that determine difficulty. Arabic, as an under-resourced language, suffers from a lack of such which results in material developers textbook writers relying on their intuitions experiences selecting appropriate texts. At the classroom level, teachers ofte...