Korean character recognition using a TDNN and an HMM
We present an on-line Korean character recognition method using a Time Delay Neural Network and a Hidden Markov Model. A TDNN is used to estimate a posteriori probabilities for graphemes in a character. An HMM aligns graphemes onto tentative graphemes, which are produced by heuristic segmentation points, using a modified Viterbi algorithm. The HMM contains statistic information, i.e., the frequencies of grapheme pairs and first consonants in the underlying language. We experiment on the proposed method with multi-writer cursive characters in a boxed input mode. Tests with untrained 22,500 on-line cursive characters show a recognition rate of 93.4% and speed of 1.37 s per character.
tdnn hmm grapheme viterbi algorithm statistic information on-line character recognition