ABSTRACT

Handwritten Character Recognition is software used to identify the handwritten characters and receive and interpret intelligible handwritten input from sources such as manuscript documents. The recent past several years has seen the development of many systems which are able to simulate the human brain actions. Among the many, the neural networks and the artificial intelligence are the most two important paradigms used. In this paper we propose a new algorithm for recognition of handwritten texts based on the spline function and neural network is proposed. In this approach the converse order of the handwritten character structure task is used to recognize the character. The spline function and the steepest descent methods are applied on the optimal notes to interpolate and approximate character shape. The sampled data of the handwritten text are used to obtain these optimal notes. Each character model is constructed by training the sequence of optimal notes using the neural network. Lastly the unknown input character is compared by all characters models to get the similitude scores.