Abstract
Machine printed Arabic Character Recognition System (MACRS] is concerned with recognition of machine printed alphanumeric Arabic characters. In the present work, characters have been represented (extracted) by using geometric moment invariant (3 order). The technique used in this research can be divided into three major steps. The first step is digitization and preprocessing to create connected component, detect the skew of a character image and correct it. The second is feature extraction, where geometric moment invariant features of the input, Arabic character is used to extract features. Finally, we describe an advanced system of classification using probabilistic neural networks structure which yields significant speed improvements. MACRS is tested using 2961 patterns for a total 141 classes with roughly 21 patterns in each class. It is important to note here that the system performs extremely well with recognition rates ranging between 84% and 88% on different folds and the overall recognition is 85.8%. This is a very good performance taking into account the fact that we have a limited number of samples in each class and that, the recognition on the training data is also extremely high (99.8%) which represents a very good training.
