Combination of statistical and neural classifiers for a high‐accuracy recognition of large character sets
Yoshimasa Kimura, Toru Wakahara, Akira Tomono · Systems and Computers in Japan · 2005
Abstract In this paper the authors propose a method for high‐accuracy recognition of large character sets using a new combination of a statistical method and neural networks. In their method, a hierarchical structure that has several neural networks arranged in a line after the statistical method is used. First, recognition using a statistical method is performed, and this represents the final result if the top candidate does not belong to a predefined set of similar characters. If it does, then the input character is discriminated in a neural network which designates the top candidate by determining the similar characters. The results are output as final results. The basic idea of this method is the functional division of a statistical method and neural networks, and the use of a neural network as determined by a statistical method. The results of recognizing 3201 character types including JIS‐1 Kanji showed an improvement in the correct recognition rate due to the combined use of a statistical method and neural networks, thereby demonstrating the validity of the authors' approach. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(9): 97–107, 2005; Published online in Wiley InterScience ( www.interscience. wiley.com ). DOI 10.1002/scj.20330