Many text recognition systems recognize text imagery at the character level and assemble words from the recognized characters. An alternative approach is to recognize text imagery at the word level, without analyzing individual characters. This approach avoids the problem of individual character segmentation, and can overcome local errors in character recognition. A word-level recognition syste...

In this paper, we propose a new font and size identification method for ultra-low resolution Arabic word images using a stochastic approach. The literature has proved the difficulty for Arabic text recognition systems to treat multi-font and multi-size word images. This is due to the variability induced by some font family, in addition to the inherent difficulties of Arabic writing including cu...

In recent years, rapidly developed hand written word recognition techniques have attracted researcher’s attention to study Arabic word classification. Arabic language has cursive style of writing so it needs special framework for classification. In this paper, a precise framework for Arabic word classification is presented, which uses sparse coding with spatial pyramid matching (SPM) algorithm ...

We explore the effect of using Arabic semantic equivalents in an example-based Arabic-English translation system. We describe two experiments using single-word equivalents in translation as test cases for broadening the level of similarity and using multi-word Arabic paraphrases in the future. In the first experiment, we use synonymous Arabic nouns, derived from a lexicon, to help locate potent...

This article presents neurolinguistic data on word stress perception in Cairene Arabic, in comparison to previous results on German and Turkish. The main goal is to investigate how central properties of stress systems such as predictability of stress and metrical structure are reflected in the prosodic processing of words. Cairene Arabic is a language with a regular foot-based word stress syste...

Arabic script is naturally cursive and unconstrained and, as a result, an automatic recognition of its handwriting is a challenging problem. The analysis of Arabic script is further complicated in comparison to Latin script due to obligatory dots/stokes that are placed above or below most letters. In this paper, we introduce a new approach that performs online Arabic word recognition on a conti...

Arabic language is very rich in derivations, vocabulary, and grammatical structures. The problem of determining the correct meaning of a word in a non-vowelized Arabic sentence is not a trivial task since Arabic is very rich in the polysemy phenomena. This paper attempts to reveal the word sense ambiguity, by building a semantic parser powered by a statistical semantic analyzer, which may aid i...

This chapter presents an adaptation of existing techniques in Arabic morphology by leveraging corpus statistics to make them suitable for Information Retrieval (IR). The adaptation resulted in the development of Sebawai, an shallow Arabic morphological analyzer, and Al-Stem, an Arabic light stemmer. Both were used to produce Arabic index terms for Arabic experimentation. Sebawai is concerned wi...

Study Design: A prospective 1-year study of whiplash patients presenting with either isolated neck pain (WADI/II), or neck pain with neurological signs/or symptoms (WADIII). Objective: We hypothesize that WADI/II and WADIII are distinct entities with significant differences in clinical presentation, pathoanatomy, and prognosis. Summary of Background Data: Whiplash associated disorders (WAD) are...

We describe the CMU submission for the 2014 shared task on language identification in code-switched data. We participated in all four language pairs: Spanish–English, Mandarin–English, Nepali–English, and Modern Standard Arabic–Arabic dialects. After describing our CRF-based baseline system, we discuss three extensions for learning from unlabeled data: semi-supervised learning, word embeddings,...