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This paper presents a new approach to compensating affine distortion of an isolated online handwritten Chinese character. The input sample is first analyzed by using a character-structure-guided orientation estimation approach. If necessary, the orientation hypotheses are refined based on confidence evaluation of two pre-classifiers. Depending on the number of possible orientations, an HMM-based minimax...
We present a new feature extraction approach to online Chinese handwriting recognition based on continuous-density hidden Markov models (CDHMM). Given an online handwriting sample, a sequence of time-ordered dominant points are extracted first, which include stroke-endings, points corresponding to local extrema of curvature, and points with a large distance to the chords formed by pairs of previously...
This paper presents a new approach to large-vocabulary online handwritten Chinese character recognition based on semi-tied covariance (STC) modeling. Detailed procedures are described for estimating the STC model parameters under both maximum likelihood (ML) and minimum classification error (MCE) criteria. Compared with the state-of-the-art modified quadratic discriminant function (MQDF) based classifiers,...
Nonlinear shape normalization (NSN) approaches based on line density equalization have been the most popular choice for both offline and online handwritten Chinese character recognition (HCCR). However, in a recent study of using 8-directional features for online HCCR, we discovered that an NSN approach based on dot density equalization achieved a much better performance than that of an NSN approach...
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