Verification of Graphemes Using Neural Networks for HMM-based On-line Handwritten Hangul Recognition
Sung-Jung Cho, JIN H. KIM · International Journal of Computer Processing Of Languages · 2002
In this paper, we propose a grapheme verifier based on a neural network (NN) for on-line handwritten Hangul (Korean script) recognition. The verifier complements the global information and discrimination power to hidden Markov models (HMMs) by penalizing incorrect inputs and passing correct ones intact. It takes structural features from partial inputs aligned to HMM states as global information and gives the probability of their validity; one if they belong to the grapheme and zero if they do not. Hangul syllable characters are recognized with the network of grapheme models which are composed of HMM recognizers and NN verifiers. Probabilities of verifiers are integrated with those of HMMs during the search for the most probable path in the network. Experimental result showed that the proposed verifiers effectively reduced recognition errors of HMM recognizers for Hangul syllable characters by 39.2% on average.