Character segmentation is a crucial step in most Arabic optical text recognition systems. The recognition process depends mainly on the accuracy of the character segmentation. This paper presents a novel adaptive algorithm for the off-line segmentation of printed Arabic text. There are many challenging features in the Arabic writing, for example, it is cursive and characters in a word can take ...

This paper aims to implement a Support Vector Machines (SVMs) based text classification system for Arabic language articles. This classifier uses CHI square method as a feature selection method in the pre-processing step of the Text Classification system design procedure. Comparing to other classification methods, our system shows a high classification effectiveness for Arabic data set in term ...

Arabic Sign Language (ArSL) is the native language for the Arab deaf community. ArSL allows deaf people to communicate among themselves and with non-deaf people around them to express their needs, thoughts and feelings. Opposite to spoken languages, Sign Language (SL) depends on hands and facial expression to express the thought instead of sounds. In recent years, interest in translating sign l...

It is challenging to translate names and technical terms from English into Arabic. Translation is usually done phonetically: different alphabets and sound inventories force various compromises. For example, Peter Streams may come out as hr..~ ~ bytr szrymz. This process is called transliteration. We address here the reverse problem: given a foreign name or loanword in Arabic text, we want to re...

This article introduces a methodology for analyzing sentiment in Arabic text using a global foreign lexical source. Our method leverages the available resource in another language such as the SentiWordNet in English to the limited language resource that is Arabic. The knowledge that is taken from the external resource will be injected into the feature model whilethe machine-learning-based class...

In the paper, there are analyzed and compared results of usable methods for discrepancies detection based on character n-gram profiles (the set of character n-gram normalized frequencies of a text) for English and Arabic documents. English and Arabic texts were analyzed from many statistical characteristics point of view. We covered some statistical differences between both languages and we app...

This paper describes a system for the recognition of Arabic text in document images. The system is designed to perform well on low resolution and low quality document images. On a set of 138 page images digitized at 200x200 dpi the system achieved a 93% correct character recognition rate. On the same pages digitized at 100x200 dpi, the system achieved an 89% character recognition rate. The syst...

We propose a machine learning method for recognizing modern Arabic poems based on the common poetic features of modern Arabic poetry. The poetic features include: rhyming, repetition, use of diacritics and punctuations, and text alignment. The method can classify text documents as poem or non-poem documents with a very high accuracy of 99.81%.

special wisdom from the Ruler of the world". A German Foreword (i-xii) is followed by an Introduction (pp. 1-56, numerals in English). Weisser discusses briefly problems related to Sirr al-khalTqa, and gives a detailed description and an appraisal of sixteen Arabic manuscripts, from which she selected four in order to establish the Arabic text. This Introduction also provides footnotes and an e...