  The use of OCR in postal services is not yet universal and there are still
many countries that process mail sorting manually. Automated Arabic/Indian
numeral Optical Character Recognition (OCR) systems for Postal services are
being used in some countries, but still there are errors during the mail
sorting process, thus causing a reduction in efficiency. The need to
investigate fast and efficient recognition algorithms/systems is important so
as to correctly read the postal codes from mail addresses and to eliminate any
errors during the mail sorting stage. The objective of this study is to
recognize printed numerical postal codes from mail addresses. The proposed
system is a multistage hybrid system which consists of three different feature
extraction methods, i.e., binary, zoning, and fuzzy features, and three
different classifiers, i.e., Hamming Nets, Euclidean Distance, and Fuzzy Neural
Network Classifiers. The proposed system, systematically compares the
performance of each of these methods, and ensures that the numerals are
recognized correctly. Comprehensive results provide a very high recognition
rate, outperforming the other known developed methods in literature.
