Even today in Twenty First Century Handwritten communication has its own stand and most of the times, in daily life it is globally using as means of communication and recording the information like to be shared with others. Challenges in handwritten characters recognition wholly lie in the variation and distortion of handwritten characters, since different people may use different style of handwriting, and direction to draw the same shape of the characters of their known script. This paper demonstrates the nature of handwritten characters, conversion of handwritten data into electronic data, and the neural network approach to make machine capable of recognizing hand written characters.Key Words: Machine recognition, Handwriting recognition, neural networks. INTRODUCTIONHandwritten characters are vague in nature as there may not always be sharp perfectly straight lines, and curves not necessarily be smooth, unlikely the printed characters. Furthermore, characters can be drawn in different sizes and orientation which are often supposed to be written on a baseline in an upright or downright position. Therefore, a robust handwritten recognition system can be designed by considering these limitations. However, it is quiet tedious sometimes to recognize hand written characters as it can be seen that most of the people can not even read their own written notes. Therefore, there is an obligation for a writer to write clearly. But even today in Twenty First Century Handwritten communication has its own stand and most of the times, in daily life it is globally using as means of communication and recording the information like to be shared with others.Researchers already paid many efforts in designing hand written character recognition system most of them cited as [1-5] because of its important application like bank checking process, reading postal codes and reading different forms [6]. Handwritten digit recognition is still a problem for many languages like Arabic, Farsi, Chinese, English, etc [7]. A machine can perform more tasks than a human being in the same time; this kind of application saves time and money and eliminates the requirement that a human perform such a repetitive task. For the recognition of English handwritten characters, various methods have been proposed [8][9][10][11][12]. Also a few numbers of studies have been reported for Farsi language [13][14][15]. Proposed Hand written character recognition system for machine recognition can be developed in these phases: scanning of hand written characters i.e conversion into electronic data, usually an black & white image file; some preprocessing can be applied to the image; then the feature of the character will be extracted from the image; finally, on the basis of extracted features from the image, the character can be classify to recognize using gradient descent learning method for feed forward neural network. In next sections we explore the proposed hand written character recognition system step by step. Finally, in the last section results will be...

This paper demonstrates the use of neural networks for developing a system that can recognize hand-written English alphabets. In this system, each English alphabet is represented by binary values that are used as input to a simple feature extraction system, whose output is fed to our neural network system. KeywordsNeural network pattern recognition, hand written character recognition.

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Energy management is the prime issue in the domain of Sensor networks where the deployment process requires a continuous resource of energy. Most of the energy required during communication over the network results a need of energy conserving routing protocols in sensor networks.

Paper demonstrates the stochastic optimal control model to enhance immune system response. Immune system response can be amplified by agents that kill the pathogen, which stimulates the production of antibodies and implies the enhancement in the health of the organ. Imperfect measurements of the dynamic state degrade the precision of feedback adjustments to therapy; however, optimal state estimation allows the feedback strategy to be implemented with incomplete measurements and minimizes the expected effects of measurement error. The stochastic approach with genetic computing is evaluated to minimize the mutiobjective treatment cost function.