Optical character recognition (OCR) is the process of extracting handwritten or printed text from a scanned or printed image and converting it to a machine-readable form for further data processing, such as searching or editing. Automatic text extraction using OCR helps to digitize documents for improved productivity and accessibility and for preservation of historical documents. This paper provides a survey of the current state-of-the-art applications, techniques, and challenges in Arabic OCR. We present the existing methods for each step of the complete OCR process to identify the best-performing approach for improved results. This paper follows the keyword-search method for reviewing the articles related to Arabic OCR, including the backward and forward citations of the article. In addition to state-of-art techniques, this paper identifies research gaps and presents future directions for Arabic OCR.

The sale of counterfeit medicine is a continuously growing global problem costing billions of dollars and effecting hundreds of thousands of precious human lives annually. Consumers of medicine have no reliable and simple way of distinguishing genuine medicine from counterfeit. In this paper we analyzed existing techniques of counterfeit medicine identification on the bases of usability, security and scalability. Different shortcomings and security weaknesses of these reviewed solutions are identified. Based on these observations, a set of requirements is determined. These requirements address different aspects of a complete solution, such as usability by end users, security of the system against various types of attacks and scalability of the solution. We then propose a framework for reliably ensuring the fidelity of purchased medicine. The proposed framework is simple to use for consumers and does not require specialized equipment, training or technology. The proposed framework will enable the consumers to verify the legitimateness of their purchased medication by using their mobile phones. Data matrix is used to make the verification code machine readable.

Software Defined Data Center Networks utilize a large number of rules to define routes for network flows. SDN controllers install rules at each switch in order to maximize metrics such as efficiency, delay or energy consumption. This requires each switch to store a substantial number of rules, which is not possible for current Top-of-Rack (ToR) switches as they usually have capacities of 2-4k rules. In this paper we present T-Flex (short for flexible TCAM), a technique for implementing virtual TCAM for data center switches. T-Flex can enhance the rule storage capabilities of a switch by up to 10x, and can forward incoming flows at line rate under most circumstances. Moreover, it provides full OpenFlow compatibility for use in SDN environments, and has been tested to work on a data center production grade Intel switch. Designed as an extension to virtual switches, T-Flex can provide the illusion of an unlimited TCAM in a data center switch.