Neural networks in the recognition of machine printed Arabic characters

The objective of this study is to analyze and compare three different recognition approaches to machine printed Arabic characters. The first approach is a template matching and a correlation technique where an input character is compared to a standard set of stored prototype images. The second and the third approaches are based on feature analysis and matching. The features in the second approach are extracted from the horizontal and vertical projections of the images of characters. The third approach is a structural approach where the features are extracted from the geometry of the segments that make a character. In all approaches, the same neural network structure, feedforward with the back propagation learning algorithm, is used for classification. A centering and scaling normalization preprocessing stage precedes the feature extraction process and is used to achieve a size and a position invariant recognition system. The study focuses on the 28 basic Arabic characters of the Cairo font. The performance of the recognition algorithms in each approach is evaluated and the results are compared.