| Project/Area Number |
12640133
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
General mathematics (including Probability theory/Statistical mathematics)
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| Research Institution | Oita University |
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Principal Investigator |
FUKUDA Ryoji Oita University, Faculty of Engineering, Assistant Professor, 工学部, 助教授 (70238492)
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| Co-Investigator(Kenkyū-buntansha) |
OBATA Tsuneshi Kyushu University, Faculty of Mathematics, Assistant, 工学部, 助手 (00244153)
HARA Takahiro Kyushu University, Faculty of Mathematics, Lecutrer, 工学部, 講師 (90208653)
SUZUKI Masakazu Kyushu University, Faculty of Mathematics, Assistant Professor, 大学院・数理学研究院, 教授 (20112302)
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| Project Period (FY) |
2000 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥3,600,000 (Direct Cost: ¥3,600,000)
Fiscal Year 2002: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2001: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2000: ¥1,400,000 (Direct Cost: ¥1,400,000)
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| Keywords | Character Recognition / Graph Recognition / Stochastic Model / データ予測 / 数式認識 / 太字判定 / フォント認識 / OCR / 投票 |
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| Research Abstract |
The technique for the recognition of simple binary images, character recognition is its main example, often uses certain statistical features. Many of these methods may depend on individual experiences, so that it is very hard to find a general theory for them. Then, we consider these techniques by the distributions of following 3 types.
1. Distribution as a figure (Actually, it is a multidimensional distribution through some feature vector.)
2. Distribtuion as a character symbol in the document
3. Distribution of the recognition system
The aim of this research is to find some general theories underlying these techniques by analyzing various aspects of these distribution. As the result, we have developed the technique of classifying the line types is character images, recoginiton of images of line graphics, and combined several recognition methods using the distribution of recognition order of each recognition method. In addition, we applied these techniques to the data prediction of measured dying shrinkage strain of concrete specimens.
And the performance of the recognition of the document including the mathematical expression input by image scanner, using some statistical techniques. In addition, for the voting method, it advanced further analysis from the theoretical standpoint and applied them for the analysis of human mind inherent in the questionnaire data.
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