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During the last several years there have been developed many systems which are able to simulate the human brain behavior. To achieve this goal, two of the most important paradigms used, are the Neural Networks and the Artificial Intelligence. Both of them are primary tools for development of systems to capable of performing tasks such as: handwritten characters, voice, faces, signatures recognition and so many other biometric applications that have attracted considerable attention during the last few years. In this paper a new algorithm for cursive handwritten characters recognition based on the Spline function is proposed, in which the inverse order of the handwritten character construction task will be used to recognize the character. From the sampled data obtained by using a digitizer board, the sequence of the most significant points (optimal knots) of the handwriting character will be obtain, and then the natural Spline function and the steepest descent method will be used to interpolate and approximate character shape, Using a training set consisting of the sequence of optimal knots, each character model will be constructed. Finally the unknown input character will be compared by all characters models to get the similitude scores. The character model with higher similitude score will be considered as the recognized character of the input data. The proposed system is evaluated by computer simulation and simulation results show the global recognition rate with 93.5%.