VERIFICATION OF GRAPHEMES USING NEURAL NETWORKS IN AN HMM­BASED ON­LINE KOREAN HANDWRITING RECOGNITION SYSTEM

This paper presents a neural network based verification method in an HMM­based on­line Korean handwriting recognition system. It penalizes unreasonable grapheme hypotheses and complements global and structural information to the HMM­based recognition system, which is intrinsically based on local information. In the proposed system, each grapheme has one neural network verifier as well as one HMM recognizer. The verifier takes as an input the grapheme hypothesis generated by the HMM and outputs a posteriori probability as its validity. This probability is then incorporated into the search process by Viterbi algorithm during recognition. The global and structural information to the verifier is obtained from the relationship between primitive strokes in each grapheme by analyzing their correspondence with the HMM states. The experimental result shows that the recognition error of the baseline HMM network can be reduced by 39.2% with the proposed verification scheme