Presentation | 2009-12-17 Accelerated Character Recognition Using a Regression-Tree Molding Takahiro OTA, Toshikazu WADA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | High-speed and accurate character recognition is required for monitoring the condition of Ink Jet Printer (IJP) for industrial use, which is mainly used in production lines. However, there is no system that meets these requirements in commercially available character recognition systems. In this paper, we first propose, "modified compound similarity method", which is a high-accuracy but slow classifier. Next, we propose its acceleration method by learning its input-output relationship using linear regression trees and replacing the classifier by the learnt regression tree. We call this method is "Molding". For the molding a large amount of learning data is necessary. Then, we generated training data that imitates the behavior of IJP's feature variation. In the experiment, we confirmed that 500-1000 times speed up is achieved without dropping the recognition rate. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Subspace method / Compound similarity method / PaLM-tree |
Paper # | PRMU2009-134 |
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Conference Information | |
Committee | PRMU |
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Conference Date | 2009/12/10(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Pattern Recognition and Media Understanding (PRMU) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Accelerated Character Recognition Using a Regression-Tree Molding |
Sub Title (in English) | |
Keyword(1) | Subspace method |
Keyword(2) | Compound similarity method |
Keyword(3) | PaLM-tree |
1st Author's Name | Takahiro OTA |
1st Author's Affiliation | Wakayama University:Kishugikenkogyo Co., Ltd() |
2nd Author's Name | Toshikazu WADA |
2nd Author's Affiliation | Wakayama University |
Date | 2009-12-17 |
Paper # | PRMU2009-134 |
Volume (vol) | vol.109 |
Number (no) | 344 |
Page | pp.pp.- |
#Pages | 6 |
Date of Issue |