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ZONE THEORY APPLIED TO BOOST RECOGNITION RATE OF HMM BASED SYSTEM

Character recognition has attracted the attention of researchers since the early days of computing. Since then, a number of successful systems have been forwarded. Study reveals that a promising recognition system should have two essential factors viz. a good combination of features and a strong classifier. So far the matter of combination of features is there; in this paper features based on the concept of Zone theory have been applied in unison with Gradient, Projection and curvature features to represent a character image efficiently.The success of automatic speech recognition systems based on HMM has motivated recent attempts to apply similar methods to character recognition. It has been found that the limited recognition rate of 1D HMM Character Recognition System and requirement of respectively larger no. of samples for construction of 2D HMM system, HMM based character recognition system is lagging behind other systems so far assured practical application is concerned. The purpose of this research work is to enhance the recognition rate of an off-line handwritten Character Recognition System using 1D HMM with an ideal combination of features followed by decreased algorithm complexity, so that the processing time could be reduced (very close to real time system) while providing higher recognition accuracy. Ultimately, it could be implemented practically with full confidence. The overall efficiency of the system has been found to be 98.46%