Abstract-Online recognition of Arabic handwritten text has been an ongoing research problem for many years. Generally, online text recognition field has been gaining more interest lately due to the increasing popularity of hand-held computers, digital notebooks and advanced cellular phones. Most of the online text recognition systems consist of three main phases which are preprocessing, feature extraction, and recognition phase. This paper compares between different techniques that have been used to extract the features of Arabic handwriting scripts in online recognition systems. Those techniques attempt to extract the feature vector of Arabic handwritten words, characters, numbers or strokes. This vector then will be fed into the recognition engine to recognize the pattern using the feature vector. The structure and strategy of those reviewed techniques are explained in this article. The strengths and weaknesses of using these techniques will also be discussed.

Abstract-Online recognition of Arabic handwritten text has been an on-going research problem for many years. Generally, online text recognition field has been gaining more interest lately due to the increasing popularity of hand-held computers, digital notebooks and advanced cellular phones. However, different techniques have been used to build several online handwritten recognition systems for Arabic text, such as Neural Networks, Hidden Markov Model, Template Matching and others. Most of the researches on online text recognition have divided the recognition system into these three main phases which are preprocessing phase, feature extraction phase and recognition phase which considers as the most important phase and the heart of the whole system. This paper presents and compares techniques that have been used to recognize the Arabic handwriting scripts in online recognition systems. Those techniques attempt to recognize Arabic handwritten words, characters, digits or strokes. The structure and strategy of those reviewed techniques are explained in this article. The strengths and weaknesses of using these techniques will also be discussed.

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<p class="0abstract">Due to the increased rate of fire accidents which cause many damages and losses to people souls, material, and property in Basra city. The necessity of analyzing and mining the data of the fire accidents became an urgent need to find a solution. The need increased for a solution that helps to mitigate and reduce the number of accidents. In this paper, data mining techniques and applications including data preprocessing, data cleaning, and data exploration have been applied. Data mining applications is performed to analyze and discover the hidden knowledge in ten years of data (fire accidents happened from 2010 – 2019) which is approximately 20k record of accidents. These data mining techniques along with the association rules algorithm is applied on the dataset. The applied approach and techniques resulted in discovering the patterns and the nature of the fire accidents in Basra city. It also helped to reach to recommendations and resolutions for mitigating the fire accidents and its occurrence rate.</p>