New Approaches for Cursive Languages Recognition : Machine and Hand Written Scripts and Texts

Three different approaches are considered in this paper to deal with the methods of Pattern Classification and Recognition. The main patterns considered are images representing the alphabet of cursive-scripts languages, particularly Arabic alphabet. The practical results of written scripts recognition led to the possibility of applying the main ideas and criteria to written and spoken texts and hence to generalise the worked out algorithms and approaches and extend them to test other kinds of images. Key-Words: Scripts and Texts Recognition, Toeplitz Matrices, Neural Networks Approaches.