This paper shows that when a pattern matching method used in optical character readers is highly accurate, it can be used effectively in on-line Japanese character recognition. Stroke matching methods used in previous conventional on-line character recognition have restricted the number and the order of strokes. On the other hand, orientation-feature pattern matching methods avoid these restrictions. The authors have improved a pattern matching method with the development in the flexible pattern matching (FPM) method, based on nonlinear shape normalization and nonlinear pattern matching, which includes the normalization-cooperative feature extraction (NCFE) method. These improvements have increased the recognition rate from 81.9% to 95.9%, when applied to the off-line database ETL-9 from the Electrotechnical Laboratory, Japan. When applied on-line to the examination of 151,533 Kanji and Hiragana characters in 3,036 categories, the recognition rate achieved 94.0%, while the cumulative recognition rate within the best ten candidates was 99.1%.

An integrated pen interface system was developed to allow effective Japanese text entry. It consists of sub-systems for handwriting recognition, contextual post-processing, and enhanced Kana-to-Kanji conversion. The recognition sub-system uses a hybrid algorithm consisting of a pattern matcher and a neural network discriminator. Special care was taken to improve the recognition of non-Kanji and simple Kanji characters frequently used in fast data entry. The post-processor predicts consecutive characters on the basis of bigrams modified by the addition of parts of speech and substitution of macro characters for Kanji characters. A Kana-to Kanji conversion method designed for ease of use with a pen interface has also been integrated into the system. In an experiment in which 2,900 samples of Kanji and non-Kanji characters were obtained from 20 subjects, it was observed that the original recognition accuracy of 83.7% (the result obtained by using the pattern matching recognizer) was improved to 90.7% by adding the neural network discriminator, and that it was further improved to 94.4% by adding the post-processor. The improved recognition accuracy for non-Kanji characters was particularly marked.