Statistical Character Structure Modeling and Its Application to Handwritten Chinese Character Recognition.
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| Title: | Statistical Character Structure Modeling and Its Application to Handwritten Chinese Character Recognition. |
|---|---|
| Authors: | In-Jung Kim1 ijkim@ai.kaist.ac.kr, Jin-Hyung Kim, Jean-Denis2 jkim@ai.kaist.ac.kr |
| Source: | IEEE Transactions on Pattern Analysis & Machine Intelligence. Nov2003, Vol. 25 Issue 11, p1422-1436. 15p. 2 Black and White Photographs, 21 Diagrams. |
| Subjects: | Chinese character sets (Data processing), Image analysis, Graphology, Heuristic programming, Algorithms, Pattern recognition systems |
| Abstract: | This paper proposes a statistical character structure modeling method. It represents each stroke by the distribution of the feature points. The character structure is represented by the joint distribution of the component strokes. In the proposed model, the stroke relationship is effectively reflected by the statistical dependency. It can represent all kinds of stroke relationship effectively in a systematic way. Based on the character representation, a stroke neighbor selection method is also proposed. It measures the importance of a stroke relationship by the mutual information among the strokes. With such a measure, the important neighbor relationships are selected by the nth order probability approximation method. The neighbor selection algorithm reduces the complexity significantly because we can reflect only some important relationships instead of all existing relationships. The proposed character modeling method was applied to a handwritten Chinese character recognition system. Applying a model-driven stroke extraction algorithm that cooperates with a selective matching algorithm, the proposed system is better than conventional structural recognition systems in analyzing degraded images. The effectiveness of the proposed methods was visualized by the experiments. The proposed method successfully detected and reflected the stroke relationships that seemed intuitively important. The overall recognition rate was 98.45 percent, which confirms the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Pattern Analysis & Machine Intelligence is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 11271616 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Statistical Character Structure Modeling and Its Application to Handwritten Chinese Character Recognition. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22In-Jung+Kim%22">In-Jung Kim</searchLink><relatesTo>1</relatesTo><i> ijkim@ai.kaist.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Jin-Hyung+Kim%2C+Jean-Denis%22">Jin-Hyung Kim, Jean-Denis</searchLink><relatesTo>2</relatesTo><i> jkim@ai.kaist.ac.kr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Pattern+Analysis+%26+Machine+Intelligence%22">IEEE Transactions on Pattern Analysis & Machine Intelligence</searchLink>. Nov2003, Vol. 25 Issue 11, p1422-1436. 15p. 2 Black and White Photographs, 21 Diagrams. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Chinese+character+sets+%28Data+processing%29%22">Chinese character sets (Data processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Graphology%22">Graphology</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic+programming%22">Heuristic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+recognition+systems%22">Pattern recognition systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper proposes a statistical character structure modeling method. It represents each stroke by the distribution of the feature points. The character structure is represented by the joint distribution of the component strokes. In the proposed model, the stroke relationship is effectively reflected by the statistical dependency. It can represent all kinds of stroke relationship effectively in a systematic way. Based on the character representation, a stroke neighbor selection method is also proposed. It measures the importance of a stroke relationship by the mutual information among the strokes. With such a measure, the important neighbor relationships are selected by the nth order probability approximation method. The neighbor selection algorithm reduces the complexity significantly because we can reflect only some important relationships instead of all existing relationships. The proposed character modeling method was applied to a handwritten Chinese character recognition system. Applying a model-driven stroke extraction algorithm that cooperates with a selective matching algorithm, the proposed system is better than conventional structural recognition systems in analyzing degraded images. The effectiveness of the proposed methods was visualized by the experiments. The proposed method successfully detected and reflected the stroke relationships that seemed intuitively important. The overall recognition rate was 98.45 percent, which confirms the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Pattern Analysis & Machine Intelligence is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TPAMI.2003.1240117 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1422 Subjects: – SubjectFull: Chinese character sets (Data processing) Type: general – SubjectFull: Image analysis Type: general – SubjectFull: Graphology Type: general – SubjectFull: Heuristic programming Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Pattern recognition systems Type: general Titles: – TitleFull: Statistical Character Structure Modeling and Its Application to Handwritten Chinese Character Recognition. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: In-Jung Kim – PersonEntity: Name: NameFull: Jin-Hyung Kim, Jean-Denis IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2003 Type: published Y: 2003 Identifiers: – Type: issn-print Value: 01628828 Numbering: – Type: volume Value: 25 – Type: issue Value: 11 Titles: – TitleFull: IEEE Transactions on Pattern Analysis & Machine Intelligence Type: main |
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