This paper presents a query-by-example word spotting in handwritten Arabic documents, based on Scale Invariant Feature Transform (SIFT), without using any text word or line segmentation approach, because any errors affect to the subsequent word representation. First the interest points are automatically extracted from the images using SIFT detector, then, we use SIFT descriptor to represent eac...

We present in this work a new approach for the Automatic diacritization for Arabic texts using three stages. During the first phase, we integrated a lexical database containing the most frequent words of Arabic with morphological analysis by Alkhalil Morpho Sys which provided possible diacritization for each word. The objective of the second module is to eliminate the ambiguity using a statisti...

Nahla A Belal CBAS: Context Based Arabic Stemmer Arabic morphology encapsulates many valuable features such as word’s root. Arabic roots are being utilized for many tasks the process of extracting a word’s root is referred to as stemming. Stemming is an essential part of most Natural Language Processing tasks, especially for derivative languages such as Arabic. However, stemming is faced with t...

Automatic off-line Arabic handwriting recognition still faces a big challenges. Due to the cursive nature of the Arabic language, most of published works are based on recognition of a whole word without segmentation. This paper presents a new framework for the recognition of handwritten Arabic words based on segmentation. This framework involves two phases (training phase and testing phase). In...

We introduce a generic Language Independent Framework for Linguistic Code Switch Point Detection. The system uses the word length, character level (1, 2, 3, 4, and 5)-grams and word level unigram language models to train a conditional random fields (CRF) model for classifying input words into various languages. We test our proposed framework and compare it to the state-of-theart published syste...

In this paper, we show the progress for Arabic speech recognition by incorporating contextual information into the process of morphological decomposition. The new approach achieves lower out-of-vocabulary and word error rates when compared to our previous work, in which the morphological decomposition relies on word-level information only. We also describe how the vocalization procedure is impr...

Arabic has a very rich and complex morphology. Its appropriate morphological processing is very important for Information Retrieval (IR). In this paper, we propose a new stemming technique that tries to determine the stem of a word representing the semantic core of this word according to Arabic morphology. This method is compared to a commonly used light stemming technique which truncates a wor...

There are many known Arabic lexicons organized on different ways, each of them has a different number of Arabic words according to its organization way. This paper has used mathematical relations to count a number of Arabic words, which proofs the number of Arabic words presented by Al Farahidy. The paper also presents new way to build an electronic Arabic lexicon by using a hash function that ...

Arabic script is cursive in both handwritten and printed form. Segmentation of Arabic scriptespecially handwrittenis a very challenging task. Many difficulties arise due to the inherent characteristics of Arabic writing such as the overlapping of Arabic sub-words wherein the sub-words share the same vertical space, and vertical ligatures wherein characters are stacked upon each other in a word....

In this paper we propose a segmentation system for unconstrained Arabic online handwriting. An essential problem addressed by analytical-based word recognition system. The system is composed of two-stages the first is a newly special designed hidden Markov model (HMM) and the second is a rules based stage. In our system, handwritten words are broken up into characters by simultaneous segmentati...