Since the late 1960’s, research on recognition of unconstrained handwritten characters has made impressive progress and many systems have been developed, particularly in machine printed and on-line character recognition. However, there is still a significant performance gap between humans and machines in the recognition of off-line totally unconstrained handwritten character recognition. Generally, off-line handwritten character recognition system includes three stages: image preprocessing, feature extraction, and classification. Preprocessing is primarily used to reduce noise or variations of handwritten characters. A feature extraction is essential for data representation and extracting meaningful features for later processing. A classification stage assigns the characters to one of the several classes. Considering the influence on recognition performance, the features extraction plays a very important role in handwriting recognition. This has led to the development of a variety of features for handwritten recognition and their recognition performances. The baseline system used in this work applies a Global Approach for feature extraction combined with a Local Approach based on zoning mechanism, and uses Class-Modular architecture feed forward MLP (Multi Layer Perceptron) in the classification stage.