An effective hybrid modeling technique is designed for the offline recognition of unconstrained handwritten character texts. The features of each character written in the input are extracted and then passed to the neural network. The features of offline characters are extracted by using the statistical method. The data sets containing texts written by different people are used to train the system. Statistical methods are used to extract features like horizontal, vertical, and radial projections. These extracted features are classified by the feedforward and backpropagation algorithms. The algorithm computes gradient values to update the weight of neurons, and it tries to minimize the error by further updating weights to avoid misclassification of data. This system can efficiently recognize cursive texts and convert them into structural form.