This paper proposes a new structural technique for Arabic text recognition. The technique can be divided into five major steps: (1) preprocessing and binarization; (2) thinning; (3) binary tree construction; (4) segmentation; and (5) recognition. The advantage of this technique is that its execution does not depend on either the font or size of character. Thus, this same technique might be utilized for the recognition of machine or hand printed text. The relevant algorithm is implemented on a microcomputer. Experiments were conducted to verify the accuracy and the speed of this algorithm using about 20,000 subwords each with an average length of 3 characters. The subwords used were written using different fonts. The recognition rate obtained in the experiments indicated an accuracy of 93.38 % with a speed of 2.7 characters per second.

The paper proposes a structural technique for automatic recognition of hand printed Arabic characters. The advantages of this technique are: more efficient for large and complex sets such as Arabic characters; not expensive for feature extraction; and its execution time does not depend on either the font or the size of the characters. The algorithm was implemented on a microcomputer and tested by 10 different users. The recognition rate obtained was about 90%.

Character recognition has attracted an immense research interest. Hand printed Latin text and printed Arabic text are cursive script. One of the difficulties involved in processing such text is the segmentation of the text into distinct characters. Thereafter, the recognition of these characters. The paper proposes a new segmentation technique for Arabic text.>

A discrete-time model is presented of memory interference in multiprocessor systems using multiple-bus interconnection networks. It differs from earlier models in its ability to model variable connection time and arbitrary inter-request time. The model describes each processing element's behavior by means of a semi-Markov process, taking as input the number of processing elements, the number of memory modules, the number of buses, the mean think time of the processing elements, and the first and second moments of the connection time between processing elements and memories. The model produces as output the memory bandwidth, processing element utilization, memory module utilization, average queue length at a memory, and average waiting time experienced by a processing element while waiting to access a memory. Using the model, it is possible to analyze the interaction of the input parameters on the system performance without using a complex Markov chain; a four-state semi-Markov process is sufficient regardless of the think and connection time distributions. The accuracy and capability of the model are illustrated.

This correspondence develops two discrete memory interference models. These models, the equivalent rate model and the Markov chain model, provide for variable connection times between processors and memories if these times can be characterized by a discrete random variable X. The equivalent rate model, which is the simpler, requires only the first moment of X, while the Markov chain model requires the first and second moments. The models yield estimates of the bandwidth BW, the probability of acceptance Pa, and processor utilization Up. Both models give good estimates of BW when the coefficient of variation Cv of X is small. When Cv reaches 2.0 the Markov chain model still shows an error of less than 4 percent while the equivalent rate model exhibits a 50 percent error that, unlike the Markov chain model, continues to increase with increase in Cv. Finally, it is shown that BW drops significantly with increase in Cv. suggesting that processor-memory transfers should use a fixed block size if memory conflict is to be minimized.