Structural feature extraction using multiple bases
Identifieur interne : 000A55 ( PascalFrancis/Corpus ); précédent : 000A54; suivant : 000A56Structural feature extraction using multiple bases
Auteurs : H. NishidaSource :
- Computer vision and image understanding [ 1077-3142 ] ; 1995.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
The prime difficulty in research and development of the handwritten character recognition systems is in the variety of shape deformations. In particular, throughout more than a quarter of a century of research, it is found that some qualitative features such as quasi-topological features (convexity and concavity), directional features, and singular points (branch points and crossings) are effective in coping with variations of shapes. On the basis of this observation, Nishida and Mori (IEEE Trans. Pattern Anal. Mach. Intell. 14, 1992, 516-533; and Structured Document Image Analysis (H. S. Baird, H. Bunke, and K. Yamamoto, Eds.), pp. 139-187, Springer-Verlag, New York, 1992) proposed a method for structural description of character shapes by few components with rich features. This method is clear and rigorous, can cope with various deformations, and has been shown to be powerful in practice. Furthermore, shape prototypes (structural models) can be constructed automatically from the training data (Nishida and Mori, IEEE Trans. Pattern Anal. Mach. Intell. 15, 1993, 1298-1311). However, in the analysis of directional features, the number of directions is fixed to 4, and more directions such as 8 or 16 cannot be dealt with. For various applications of Nishida and Mori's method, we present a method for structural analysis and description of simple arcs or closed curves based on 2m-directional features (m = 2, 3, 4,...) and convex/concave features. On the other hand, software OCR systems without specialized hardware have attracted much attention recently. Based on the proposed method of structural analysis and description, we describe a software implementation of a handwritten character recognition system using multistage strategy.
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Format Inist (serveur)
| NO : | PASCAL 95-0418538 INIST |
|---|---|
| ET : | Structural feature extraction using multiple bases |
| AU : | NISHIDA (H.) |
| AF : | Univ. Aizu, school computer sci. eng./Fukushima 965-80/Japon |
| DT : | Publication en série; Niveau analytique |
| SO : | Computer vision and image understanding; ISSN 1077-3142; Etats-Unis; Da. 1995; Vol. 62; No. 1; Pp. 78-89; Bibl. 18 ref. |
| LA : | Anglais |
| EA : | The prime difficulty in research and development of the handwritten character recognition systems is in the variety of shape deformations. In particular, throughout more than a quarter of a century of research, it is found that some qualitative features such as quasi-topological features (convexity and concavity), directional features, and singular points (branch points and crossings) are effective in coping with variations of shapes. On the basis of this observation, Nishida and Mori (IEEE Trans. Pattern Anal. Mach. Intell. 14, 1992, 516-533; and Structured Document Image Analysis (H. S. Baird, H. Bunke, and K. Yamamoto, Eds.), pp. 139-187, Springer-Verlag, New York, 1992) proposed a method for structural description of character shapes by few components with rich features. This method is clear and rigorous, can cope with various deformations, and has been shown to be powerful in practice. Furthermore, shape prototypes (structural models) can be constructed automatically from the training data (Nishida and Mori, IEEE Trans. Pattern Anal. Mach. Intell. 15, 1993, 1298-1311). However, in the analysis of directional features, the number of directions is fixed to 4, and more directions such as 8 or 16 cannot be dealt with. For various applications of Nishida and Mori's method, we present a method for structural analysis and description of simple arcs or closed curves based on 2m-directional features (m = 2, 3, 4,...) and convex/concave features. On the other hand, software OCR systems without specialized hardware have attracted much attention recently. Based on the proposed method of structural analysis and description, we describe a software implementation of a handwritten character recognition system using multistage strategy. |
| CC : | 001D02C03 |
| FD : | Extraction forme; Analyse image; Analyse documentaire; Modèle structure; Analyse structurale; Reconnaissance caractère; Caractère manuscrit; Base donnée multiple |
| ED : | Pattern extraction; Image analysis; Document analysis; Structural model; Structural analysis; Character recognition; Manuscript character; Multiple data base |
| SD : | Extracción forma; Análisis imagen; Análisis documental; Modelo estructura; Análisis estructural; Reconocimiento carácter; Carácter manuscrito |
| LO : | INIST-15463A.354000051721770070 |
| ID : | 95-0418538 |
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Pascal:95-0418538Le document en format XML
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In particular, throughout more than a quarter of a century of research, it is found that some qualitative features such as quasi-topological features (convexity and concavity), directional features, and singular points (branch points and crossings) are effective in coping with variations of shapes. On the basis of this observation, Nishida and Mori (IEEE Trans. Pattern Anal. Mach. Intell. 14, 1992, 516-533; and Structured Document Image Analysis (H. S. Baird, H. Bunke, and K. Yamamoto, Eds.), pp. 139-187, Springer-Verlag, New York, 1992) proposed a method for structural description of character shapes by few components with rich features. This method is clear and rigorous, can cope with various deformations, and has been shown to be powerful in practice. Furthermore, shape prototypes (structural models) can be constructed automatically from the training data (Nishida and Mori, IEEE Trans. Pattern Anal. Mach. Intell. 15, 1993, 1298-1311). However, in the analysis of directional features, the number of directions is fixed to 4, and more directions such as 8 or 16 cannot be dealt with. For various applications of Nishida and Mori's method, we present a method for structural analysis and description of simple arcs or closed curves based on 2<sup>m</sup>-directional features (m = 2, 3, 4,...) and convex/concave features. On the other hand, software OCR systems without specialized hardware have attracted much attention recently. Based on the proposed method of structural analysis and description, we describe a software implementation of a handwritten character recognition system using multistage strategy.</div></front></TEI><inist><standard h6="B"><pA><fA01 i1="01" i2="1"><s0>1077-3142</s0></fA01><fA03 i2="1"><s0>Comput. vis. image underst.</s0></fA03><fA05><s2>62</s2></fA05><fA06><s2>1</s2></fA06><fA08 i1="01" i2="1" l="ENG"><s1>Structural feature extraction using multiple bases</s1></fA08><fA11 i1="01" i2="1"><s1>NISHIDA (H.)</s1></fA11><fA14 i1="01"><s1>Univ. 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On the basis of this observation, Nishida and Mori (IEEE Trans. Pattern Anal. Mach. Intell. 14, 1992, 516-533; and Structured Document Image Analysis (H. S. Baird, H. Bunke, and K. Yamamoto, Eds.), pp. 139-187, Springer-Verlag, New York, 1992) proposed a method for structural description of character shapes by few components with rich features. This method is clear and rigorous, can cope with various deformations, and has been shown to be powerful in practice. Furthermore, shape prototypes (structural models) can be constructed automatically from the training data (Nishida and Mori, IEEE Trans. Pattern Anal. Mach. Intell. 15, 1993, 1298-1311). However, in the analysis of directional features, the number of directions is fixed to 4, and more directions such as 8 or 16 cannot be dealt with. For various applications of Nishida and Mori's method, we present a method for structural analysis and description of simple arcs or closed curves based on 2<sup>m</sup>-directional features (m = 2, 3, 4,...) and convex/concave features. On the other hand, software OCR systems without specialized hardware have attracted much attention recently. 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Aizu, school computer sci. eng./Fukushima 965-80/Japon</AF><DT>Publication en série; Niveau analytique</DT><SO>Computer vision and image understanding; ISSN 1077-3142; Etats-Unis; Da. 1995; Vol. 62; No. 1; Pp. 78-89; Bibl. 18 ref.</SO><LA>Anglais</LA><EA>The prime difficulty in research and development of the handwritten character recognition systems is in the variety of shape deformations. In particular, throughout more than a quarter of a century of research, it is found that some qualitative features such as quasi-topological features (convexity and concavity), directional features, and singular points (branch points and crossings) are effective in coping with variations of shapes. On the basis of this observation, Nishida and Mori (IEEE Trans. Pattern Anal. Mach. Intell. 14, 1992, 516-533; and Structured Document Image Analysis (H. S. Baird, H. Bunke, and K. Yamamoto, Eds.), pp. 139-187, Springer-Verlag, New York, 1992) proposed a method for structural description of character shapes by few components with rich features. This method is clear and rigorous, can cope with various deformations, and has been shown to be powerful in practice. Furthermore, shape prototypes (structural models) can be constructed automatically from the training data (Nishida and Mori, IEEE Trans. Pattern Anal. Mach. Intell. 15, 1993, 1298-1311). However, in the analysis of directional features, the number of directions is fixed to 4, and more directions such as 8 or 16 cannot be dealt with. For various applications of Nishida and Mori's method, we present a method for structural analysis and description of simple arcs or closed curves based on 2<sup>m</sup>-directional features (m = 2, 3, 4,...) and convex/concave features. On the other hand, software OCR systems without specialized hardware have attracted much attention recently. Based on the proposed method of structural analysis and description, we describe a software implementation of a handwritten character recognition system using multistage strategy.</EA><CC>001D02C03</CC><FD>Extraction forme; Analyse image; Analyse documentaire; Modèle structure; Analyse structurale; Reconnaissance caractère; Caractère manuscrit; Base donnée multiple</FD><ED>Pattern extraction; Image analysis; Document analysis; Structural model; Structural analysis; Character recognition; Manuscript character; Multiple data base</ED><SD>Extracción forma; Análisis imagen; Análisis documental; Modelo estructura; Análisis estructural; Reconocimiento carácter; Carácter manuscrito</SD><LO>INIST-15463A.354000051721770070</LO><ID>95-0418538</ID></server></inist></record>Pour manipuler ce document sous Unix (Dilib)
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