Character recognition for Arabic texts poses a twofold challenge, segmenting words into letters and identifying the individual letters. We propose a method that combines the two tasks, using a grid of SIFT descriptors as features for classification of letters. Each word is scanned with increasing window sizes; segmentation points are set where the classifier achieves maximal confidence. Using t...

Hidden Markov Models (HMM) have been used with some success in recognizing printed Arabic words. In this paper, a complete scheme for totally unconstrained Arabic handwritten word recognition based on a Model discriminant HMM is presented. A complete system able to classify Arabic-Handwritten words of one hundred different writers is proposed and discussed. The system first attempts to remove s...

Occurring at rates up to 6-7 syllables per second, speech perception and understanding involves rapid identification of speech sounds and pre-activation of morphemes and words. Using event-related potentials (ERPs) and functional magnetic resonance imaging (fMRI), we investigated the time-course and neural sources of pre-activation of word endings as participants heard the beginning of unfoldin...

Word recognition systems use a lexicon to guide the recognition process in order to improve the recognition rate. However, as the lexicon grows, the computation time increases. In this paper, we present the Arabic word descriptor (AWD) for Arabic word shape indexing and lexicon reduction in handwritten documents. It is formed in two stages. First, the structural descriptor (SD) is computed for ...

Dialectal Arabic (DA) poses serious challenges for Natural Language Processing (NLP). The number and sophistication of tools and datasets in DA are very limited in comparison to Modern Standard Arabic (MSA) and other languages. MSA tools do not effectively model DA which makes the direct use of MSA NLP tools for handling dialects impractical. This is particularly a challenge for the creation of...

The Arabic language has a very rich morphology where a word is composed of zero or more prefixes, a stem and zero or more suffixes. This makes Arabic data sparse compared to other languages, such as English, and consequently word segmentation becomes very important for many Natural Language Processing tasks that deal with the Arabic language. We present in this paper two segmentation schemes th...

Part Of Speech (POS) tagging forms the important preprocessing step in many of the natural language processing applications such as text summarization, question answering and information retrieval system. It is the process of classifying every word in a given context to its appropriate part of speech. Different POS tagging techniques in the literature have been developed and experimented. Curre...

We present a method for incorporating arbitrary context-informed word attributes into statistical machine translation by clustering attribute-quali ed source words, and smoothing their word translation probabilities using binary decision trees. We describe two ways in which the decision trees are used in machine translation: by using the attribute-quali ed source word clusters directly, or by u...