Dissertation/Thesis Abstract

An N-gram enhanced learning classifier for Chinese character recognition
by Ayer, Eliot William, M.S., California State University, Long Beach, 2013, 53; 1524176
Abstract (Summary)

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.

Indexing (document details)
Advisor: Lam, Shui
Commitee: Ebert, Todd, Englert, Burkhard
School: California State University, Long Beach
Department: Computer Science
School Location: United States -- California
Source: MAI 52/03M(E), Masters Abstracts International
Subjects: Artificial intelligence, Computer science
Publication Number: 1524176
ISBN: 978-1-303-52153-9
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