A complexity measure based algorithm for multifont Chinese character recognition

A novel method for recognizing multifont Chinese characters is presented. After character skeleton extraction, a classification is first made according to a new complexity measure: a weighted sum of endpoints and nodes. Unlabeled feature point matching between an input character and model characters is realized by minimizing an approximate Euclidean distance. The final recognition decision is based on calculating a flexible similarity function which depends on the feature point dispersion of the considered fonts. Experiments on a database of 1000 Chinese characters have been conducted. The recognition rate exceeds 96%