Optical Character Recognition (OCR) has been considered to be a major breakthrough in man-machine communication. The function of OCR is to recognize previously scanned images that may contain typed, printed, and/or handwritten characters and to output the appropriate text document. A preprocessing stage (segmentation) is first performed on the scanned text to isolate lines from documents, words from lines, and finally characters from words. Immediately following the segmentation stage is the recognition stage in which the isolated characters are first processed for feature extraction and then fed to the classification process which tries to recognize the upcoming character based on the extracted features. In this paper, a recognition stage which consists of a three-layer neural network trained by the back- propagation algorithm is considered in the recognition of different Arabic fonts. Our approach is built around three interacting processes, one procedure for feature extraction of the upcoming character element, one declarative for heuristic clustering, and one exemplar to identify the target element based on some previously learned examples.
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