Two statistical language models have been investigated on their effectiveness in upgrading the accuracy of a Chinese character recognizer. The baseline model is one of lexical analytic nature which segments a sequence of character images according to the maximum matching of words with consideration of word binding forces. A model of bigram statistics of word-classes is then investigated and compared against the baseline model in terms of recognition rate improvement on the image recognizer. On the average, the baseline language model improves the recognition rate by about 7% while the bigram statistics model upgrades it by about 10%

A handwritten Chinese character off-line recognizer based on contextual vector quantization (CVQ) of every pixel of an unknown character image has been constructed. Each template character is represented by a codebook. When an unknown image is matched against a template character, each pixel of the image is quantized according to the associated codebook by considering not just the feature vector observed at each pixel, but those observed at its neighbors and their quantization as well. Structural information such as stroke counts observed at each pixel are captured to form a cellular feature vector. Supporting a vocabulary of 4616 simplified Chinese characters and alphanumeric and punctuation symbols, the writer-independent recognizer has an average recognition rate of 77.2 percent. Three statistical language models for postprocessing have been studied for their effectiveness in upgrading the recognition rate of the system. Among them, the CVQ-based language model is the most effective one upgrading the recognition rate by 10.4 percent on the average.

An off-line hand-written Chinese character recognizer based on contextual vector quantization (CVQ) supporting a vocabulary of 4616 Chinese characters, alphanumerics and punctuation symbols has been reported. Trained with a sample for each character from each of 100 writers and tested on texts of 160000 characters written by another 200 writers, the average recognition rate is 77.2%. Two statistical language models have been investigated in this study. Their performance in terms of their capabilities in upgrading the recognition rate by 8.8% and 12.0% respectively when used as post-processors of the recognizer are reported.

A hand-printed Chinese character recognizer based on contextual vector quantization (CVQ) has been built previously. In this paper, several properties of the recognizer are discussed. The recognition of 4516 Chinese characters has a successful rate of 91.0%. The output of the recognizer is passed to a language model which when applied to recognize a passage of about 1200 characters raises the rate from 91.5% to 97.5%.