An N-gram enhanced learning classifier for Chinese character recognition.

摘要

Fast and accurate recognition of offline Chinese characters is a problem significantly more difficult than the recognition of the English alphabet. The vastly larger set of characters and noise in handwriting require more sophisticated normalization, feature extraction, and classification methods. This thesis explores the feasibility of a fast and accurate classification and translation retrieval system. An ensemble classifier composed of k-nearest neighbors and support vector machines is used as the basis of a fast classifier to recognize Chinese and Japanese characters. In contrast to other models, this classifier incorporates contextual N-gram information directly into the classification task to increase the accuracy of the classifier.