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This paper presents a discriminative training approach to irrelevant variability normalization (IVN) based joint training of feature transforms and prototype-based classifier for recognition of online handwritten Chinese characters. A sample separation margin based minimum classification error criterion is adopted in IVN-based training, while an Rprop algorithm is used for optimizing the objective...
We present a study of discriminative training of classifiers using both maximum mutual information (MMI) and minimum classification error (MCE) criteria for online handwritten Chinese/Japanese character recognition based on continuous-density hidden Markov models. It is observed that MCE-trained classifiers can achieve a much higher recognition accuracy than that of MMI-trained ones. Benchmark results...
We present a study of designing compact recognizers of handwritten Chinese characters using multiple-prototype based classifiers. A modified Quick prop algorithm is proposed to optimize a sample-separation-margin based minimum classification error objective function. Split vector quantization technique is used to compress classifier parameters. Benchmark results are reported for classifiers with different...
In this paper, we present a new approach to minimum classification error (MCE) training of pattern classifiers with quadratic discriminant functions. First, a so-called sample separation margin (SSM) is defined for each training sample and then used to define the misclassification measure in MCE formulation. The computation of SSM can be cast as a nonlinear constrained optimization problem and solved...
In our previous work, a precision constrained Gaussian model (PCGM) was proposed for character modeling to design compact recognizers of handwritten Chinese characters. A maximum likelihood training procedure was developed to estimate model parameters from training data. In this paper, we extend the above work by using minimum classification error (MCE) training to improve recognition accuracy and...
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,...
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