Normalization is a very important step in automatic cursive handwritten word recognition. Based on an offline recognition system for Arabic handwritten words which uses a semi-continuous 1-dimensional HMM recognizer two different feature sets are presented. The dependencies of the feature sets from normalization steps is discussed and their performances are compared using the IFN/ENIT database ...

This paper provides a new model enhancing the Arabic OCR degraded text retrieval effectiveness. The proposed model based on simulating the Arabic OCR recognition mistakes on a word based approach. Then the model expands the user search query using the expected OCR errors. The resulting expanded search query gives higher precision and recall in searching Arabic OCR-Degraded text rather than the ...

This paper describes a novel Arabic Reading Enhancement Tool (ARET) for classroom use, which has been built using corpus-based Natural Language Processing in combination with expert linguistic annotation. The NLP techniques include a widely used morphological analyzer for Modern Standard Arabic to provide word-level grammatical details, and a relational database index of corpus texts to provide...

Arabic has a large number of affixes that can modify a stem to form words. In automatic speech recognition (ASR) this leads to a high out-of-vocabulary (OOV) rate for typical lexicon size, and hence a potential increase in WER. This is even more pronounced for dialects of Arabic where additional affixes are often introduced and the available data is typically sparse. To address this problem we ...

In this paper we present the first Arabic sentence dataset for on-line handwriting recognition written on tablet pc. The dataset is natural, simple and clear. Texts are sampled from daily newspapers. To collect naturally written handwriting, forms are dictated to writers. The current version of our dataset includes 154 paragraphs written by 48 writers. It contains more than 3800 words and more ...

In this paper we investigate the use of rich morphology such as word segmentation, part-of-speech tagging and diacritic restoration to improve Arabic language modeling. We enrich the context by performing morphological analysis on the word history. We use neural network models to integrate this additional information, due to their ability to handle long and enriched dependencies. We experimente...

Machine simulation of human reading has been the subject of intensive research for almost three decades. A large number of research papers and reports have already been published on Latin, Chinese and Japanese characters. However, little work has been conducted on the automatic recognition of Arabic in both on-line and o!-line, has been achieved towards the automatic recognition of Arabic chara...

Moisture content data from an infiltration experiment undertaken in the dry main stream bed of wadi Habawnah, south-west Saudi Arabia, are reported. A simple experiment enabled hydraulic properties of the multi-layer soil profile to be inferred through inversion of an unsaturated soil water simulation model. The results indicate a heterogeneous but highly transmissive profile and suggest that s...

1. The phrase [chewing khat] mentioned in the paper is a misnomer and should be replaced by "takhzeen al-qat" as chewing does not infer the exact meaning of what Yemeni people used to do. They used to do "takhzeen" which means in Arabic chewing and storing of qat for several hours. Therefore, the Arabic word takhzeen is used to properly describe this habit1. And the word qat with letter "q" is ...