  In this paper, an efficient Offline Hand Written Character Recognition
algorithm is proposed based on Associative Memory Net (AMN). The AMN used in
this work is basically auto associative. The implementation is carried out
completely in 'C' language. To make the system perform to its best with minimal
computation time, a Parallel algorithm is also developed using an API package
OpenMP. Characters are mainly English alphabets (Small (26), Capital (26))
collected from system (52) and from different persons (52). The characters
collected from system are used to train the AMN and characters collected from
different persons are used for testing the recognition ability of the net. The
detailed analysis showed that the network recognizes the hand written
characters with recognition rate of 72.20% in average case. However, in best
case, it recognizes the collected hand written characters with 88.5%. The
developed network consumes 3.57 sec (average) in Serial implementation and 1.16
sec (average) in Parallel implementation using OpenMP.
