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

Time Efficient Approach To Offline Hand Written Character Recognition Using Associative Memory Net