Time Efficient Approach To Offline Hand Written Character Recognition Using Associative Memory Net
This is an incremental improvement for offline handwritten character recognition systems, focusing on computational efficiency.
The authors tackled offline handwritten character recognition by developing an associative memory network with parallel processing, achieving 72.2% average recognition accuracy and reducing processing time from 3.57 seconds to 1.16 seconds with parallelization.
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.