1999 Fiscal Year Final Research Report Summary
Study on Recognition Mechanism of Transformation Character Pattern by Neural Network
Project/Area Number |
09650434
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Single-year Grants |
Section | 一般 |
Research Field |
情報通信工学
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Research Institution | Fukuoka Institute if Technology |
Principal Investigator |
SUZAKI Kenichi Department of Computer Science and Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Professor, 情報工学部・情報工学科, 教授 (40148903)
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Project Period (FY) |
1997 – 1999
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Keywords | neural network / transformation character / recognition / back-propagation / aspect movement / learning / copy-learning / geometrical conversion |
Research Abstract |
The person is the good at recognition of the character, the sound, and the image pattern. However, even if the latest image processing technology is introduced, the pattern recognition is difficult. The study on the pattern recognition by the neural network is actively done from such a background. This study is a summary of the recognition mechanism of the character pattern seen when the aspect in the space three dimensions moves. If the aspect by which the character is seen is moved, the character receives geometrical conversion. If the aspect approaches the center of the character plane, the character pattern receives the perspective. At present, the neural network by Which such a transformation character can be recognized is not proposed. This study introduces the copy learning method into the neural network, and proposes the recognition mechanism which can recognize the transformation character. The summary of this study is as follows. (1) The relation between the input pattern resolution and the character recognition rate in the aspect movement was clarified through the experiment. This study plays elucidation of complementary of the aspect movement and the character recognition the role. (2) This study proposes a copy-learning model which can recognize standard characters from superimposed characters by only learning the standard characters. This study is scheduled development into the separation and the recognition of the superimposed character of the dirt character, the picture, and the character for the future.
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Research Products
(6 results)