In this paper, we present a new and fast state-of-the-art Arabic diacritizer that guesses the diacritics of words and then their case endings. We employ a Viterbi decoder at word-level with back-off to stem, morphological patterns, and transliteration and sequence labeling based diacritization of named entities. For case endings, we use Support Vector Machine (SVM) based ranking coupled with mo...

Diacritics (short vowels) are typically omitted when writing Arabic text, and readers have to reintroduce them correctly pronounce words. There two types of diacritics: The first core-word diacritics (CW), which specify the lexical selection, second case endings (CE), appear at end word stems generally their syntactic roles. Recovering CEs is relatively harder than recovering due inter-word dep...

In this paper, we propose an approach for the separation of overlapping and touching lines within handwritten Arabic documents. Our approach is based on the morphology analysis of the terminal letters of Arabic words. Starting from 4 categories of possible endings, we use the angular variance to follow the connection and separate the endings. The proposed separation scheme has been evaluated on...

The majority of studies on Arabic diacritization have employed textually inferred features alone. This paper proposes a novel approach, where the weighted combination of speech with a text-based model is used to allow linguistically-insensitive acoustic information to correct and complement the errors generated by the text model’s diacritic predictions. The acoustic model is based on Hidden Mar...

the word “arjulakum” in the 6th verse of sura al-ma’ida is recited in three different vowelized endings; marfu’, mansub, and majrur. the two later forms are more popular. given that the word “arjulakum’ in the majrur mode is coordinated to “ru’usikum” and in the mansub mode is coordinated to the position of “ru’usikum”, and the both possibilities would make the word “ru’usikum” object of the ve...

This paper aims to investigate the problems associated with reading Arabic as a foreign language. Workers in field of teaching language must have noticed that learners face some lead hindering or slowing down their progress skill. tried pinpoint difficulties non-native learners, and different factors pertaining those problems. An empirical tool, be described below, was used achieve objectives t...

In this paper we describe and compare two techniques for the automatic diacritization of Arabic text: First, we treat diacritization as a monotone machine translation problem, proposing and evaluating several translation and language models, including word and character-based models separately and combined as well as a model which uses statistical machine translation (SMT) to post-edit a rule-b...

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 ...

Arabic character recognition of handwriting is addressed. A novel approach for the Arabic Character Recognition is presented based on statistical analysis of a typical Arabic text is presented. Results showed that the sub-word in Arabic language is the basic pictorial block rather than the word. The method of approximate stroke sequence is applied for the recognition of some Arabic characters i...