Design Compact Recognizers of Handwritten Chinese Characters Using Precision Constrained Gaussian Models, Minimum Classification Error Training and Parameter Compression

Abstract

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 split vector quantization technique to compress model parameters. Compared with the state-of-the-art MCE-trained and compressed classifiers based on modified quadratic discriminant function, PCGM-based classifiers can achieve much better memory-accuracy tradeoff, therefore offer a good solution to designing compact handwriting recognition systems for East Asian languages such as Chinese, Japanese, and Korean.