Recognition of printed Arabic text using neural networks

The main theme of this paper is the automatic recognition of Arabic printed text using artificial neural networks in addition to conventional techniques. This approach has a number of advantages: it combines rule-based (structural) and classification tests; and feature extraction is inexpensive and execution time is independent of character font and size. The technique can be divided into three major steps: The first step is preprocessing in which the original image is transformed into a binary image utilizing a 300 dpi scanner and then forming the connected component. Second, global features of the input Arabic word are then extracted such as number of subwords, number of peaks within the subword, number of peaks within the subword, number and position of the complementary character, etc.. Finally, and artificial neural networks is used for character classification. The algorithm was implemented on a powerful MS-DOS microcomputer and written in C.