Multi-font printed Mongolian document recognition system
Identifieur interne :
000216 ( PascalFrancis/Corpus );
précédent :
000215;
suivant :
000217
Multi-font printed Mongolian document recognition system
Auteurs : LIANGRUI PENG ;
CHANGSONG LIU ;
XIAOQING DING ;
HUA WANG ;
JIANMING JINSource :
-
Proceedings of SPIE, the International Society for Optical Engineering [ 0277-786X ] ; 2009.
RBID : Pascal:09-0372245
Descripteurs français
- Pascal (Inist)
- Document imprimé,
Chine,
Bibliothèque électronique,
Reconnaissance optique caractère,
Jeu caractère,
Système expert,
Reconnaissance caractère,
Système n niveaux,
Classification automatique,
0130C,
Traitement image,
4230V.
English descriptors
Abstract
Mongolian is one of the major ethnic languages in China. Large amount of Mongolian printed documents need to be digitized in digital library and various applications. Traditional Mongolian script has unique writing style and multi-font-type variations, which bring challenges to Mongolian OCR research. As traditional Mongolian script has some characteristics, for example, one character may be part of another character, we define the character set for recognition according to the segmented components, and the components are combined into characters by rule-based post-processing module. For character recognition, a method based on visual directional feature and multi-level classifiers is presented. For character segmentation, a scheme is used to find the segmentation point by analyzing the properties of proj ection and connected components. As Mongolian has different font-types which are categorized into two major groups, the parameter of segmentation is adjusted for each group. A font-type classification method for the two font-type group is introduced. For recognition of Mongolian text mixed with Chinese and English, language identification and relevant character recognition kernels are integrated. Experiments show that the presented methods are effective. The text recognition rate is 96.9% on the test samples from practical documents with multi-font-types and mixed scripts.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
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| A05 | | | | @2 7247 |
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| A08 | 01 | 1 | ENG | @1 Multi-font printed Mongolian document recognition system |
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| A09 | 01 | 1 | ENG | @1 Document recognition and retrieval XVI : 20-22 January 2009, San Jose, California, USA |
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| A11 | 01 | 1 | | @1 LIANGRUI PENG |
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| A11 | 02 | 1 | | @1 CHANGSONG LIU |
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| A11 | 03 | 1 | | @1 XIAOQING DING |
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| A11 | 04 | 1 | | @1 HUA WANG |
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| A11 | 05 | 1 | | @1 JIANMING JIN |
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| A12 | 01 | 1 | | @1 BERKNER (Kathrin) @9 ed. |
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| A12 | 02 | 1 | | @1 LIKFORMAN-SULEM (Laurence) @9 ed. |
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| A14 | 01 | | | @1 Department of Electronic Engineering, Tsinghua University, Tsinghua National Laboratory for Information Science and Technology State Key Laboratory of Intelligent Technology and Systems @2 Beijing, 100084 @3 CHN @Z 1 aut. @Z 2 aut. @Z 3 aut. @Z 4 aut. @Z 5 aut. |
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| A18 | 01 | 1 | | @1 IS & T--the Society for Imaging Science and Technology @3 USA @9 org-cong. |
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| A18 | 02 | 1 | | @1 SPIE @3 USA @9 org-cong. |
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| A18 | 03 | 1 | | @1 Ricoh Innovations @3 INC @9 org-cong. |
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| A21 | | | | @1 2009 |
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| C01 | 01 | | ENG | @0 Mongolian is one of the major ethnic languages in China. Large amount of Mongolian printed documents need to be digitized in digital library and various applications. Traditional Mongolian script has unique writing style and multi-font-type variations, which bring challenges to Mongolian OCR research. As traditional Mongolian script has some characteristics, for example, one character may be part of another character, we define the character set for recognition according to the segmented components, and the components are combined into characters by rule-based post-processing module. For character recognition, a method based on visual directional feature and multi-level classifiers is presented. For character segmentation, a scheme is used to find the segmentation point by analyzing the properties of proj ection and connected components. As Mongolian has different font-types which are categorized into two major groups, the parameter of segmentation is adjusted for each group. A font-type classification method for the two font-type group is introduced. For recognition of Mongolian text mixed with Chinese and English, language identification and relevant character recognition kernels are integrated. Experiments show that the presented methods are effective. The text recognition rate is 96.9% on the test samples from practical documents with multi-font-types and mixed scripts. |
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| C03 | 08 | X | FRE | @0 Système n niveaux @5 68 |
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| C03 | 08 | X | ENG | @0 Multilevel system @5 68 |
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Format Inist (serveur)
| NO : | PASCAL 09-0372245 INIST |
|---|
| ET : | Multi-font printed Mongolian document recognition system |
|---|
| AU : | LIANGRUI PENG; CHANGSONG LIU; XIAOQING DING; HUA WANG; JIANMING JIN; BERKNER (Kathrin); LIKFORMAN-SULEM (Laurence) |
|---|
| AF : | Department of Electronic Engineering, Tsinghua University, Tsinghua National Laboratory for Information Science and Technology State Key Laboratory of Intelligent Technology and Systems/Beijing, 100084/Chine (1 aut., 2 aut., 3 aut., 4 aut., 5 aut.) |
|---|
| DT : | Publication en série; Congrès; Niveau analytique |
|---|
| SO : | Proceedings of SPIE, the International Society for Optical Engineering; ISSN 0277-786X; Coden PSISDG; Etats-Unis; Da. 2009; Vol. 7247; 72470J.1-72470J.7; Bibl. 5 ref. |
|---|
| LA : | Anglais |
|---|
| EA : | Mongolian is one of the major ethnic languages in China. Large amount of Mongolian printed documents need to be digitized in digital library and various applications. Traditional Mongolian script has unique writing style and multi-font-type variations, which bring challenges to Mongolian OCR research. As traditional Mongolian script has some characteristics, for example, one character may be part of another character, we define the character set for recognition according to the segmented components, and the components are combined into characters by rule-based post-processing module. For character recognition, a method based on visual directional feature and multi-level classifiers is presented. For character segmentation, a scheme is used to find the segmentation point by analyzing the properties of proj ection and connected components. As Mongolian has different font-types which are categorized into two major groups, the parameter of segmentation is adjusted for each group. A font-type classification method for the two font-type group is introduced. For recognition of Mongolian text mixed with Chinese and English, language identification and relevant character recognition kernels are integrated. Experiments show that the presented methods are effective. The text recognition rate is 96.9% on the test samples from practical documents with multi-font-types and mixed scripts. |
|---|
| CC : | 001B00A30C; 001B40B30V |
|---|
| FD : | Document imprimé; Chine; Bibliothèque électronique; Reconnaissance optique caractère; Jeu caractère; Système expert; Reconnaissance caractère; Système n niveaux; Classification automatique; 0130C; Traitement image; 4230V |
|---|
| ED : | Printed document; China; Digital libraries; Optical character recognition; Character sets; Expert systems; Character recognition; Multilevel system; Automatic classification |
|---|
| SD : | Documento impreso; Sistema n niveles; Clasificación automática |
|---|
| LO : | INIST-21760.354000172953960180 |
|---|
| ID : | 09-0372245 |
|---|
Links to Exploration step
Pascal:09-0372245
Le document en format XML
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</keywords><front><div type="abstract" xml:lang="en">Mongolian is one of the major ethnic languages in China. Large amount of Mongolian printed documents need to be digitized in digital library and various applications. Traditional Mongolian script has unique writing style and multi-font-type variations, which bring challenges to Mongolian OCR research. As traditional Mongolian script has some characteristics, for example, one character may be part of another character, we define the character set for recognition according to the segmented components, and the components are combined into characters by rule-based post-processing module. For character recognition, a method based on visual directional feature and multi-level classifiers is presented. For character segmentation, a scheme is used to find the segmentation point by analyzing the properties of proj ection and connected components. As Mongolian has different font-types which are categorized into two major groups, the parameter of segmentation is adjusted for each group. A font-type classification method for the two font-type group is introduced. For recognition of Mongolian text mixed with Chinese and English, language identification and relevant character recognition kernels are integrated. Experiments show that the presented methods are effective. The text recognition rate is 96.9% on the test samples from practical documents with multi-font-types and mixed scripts.</div>
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</fA66><fC01 i1="01" l="ENG"><s0>Mongolian is one of the major ethnic languages in China. Large amount of Mongolian printed documents need to be digitized in digital library and various applications. Traditional Mongolian script has unique writing style and multi-font-type variations, which bring challenges to Mongolian OCR research. As traditional Mongolian script has some characteristics, for example, one character may be part of another character, we define the character set for recognition according to the segmented components, and the components are combined into characters by rule-based post-processing module. For character recognition, a method based on visual directional feature and multi-level classifiers is presented. For character segmentation, a scheme is used to find the segmentation point by analyzing the properties of proj ection and connected components. As Mongolian has different font-types which are categorized into two major groups, the parameter of segmentation is adjusted for each group. A font-type classification method for the two font-type group is introduced. For recognition of Mongolian text mixed with Chinese and English, language identification and relevant character recognition kernels are integrated. Experiments show that the presented methods are effective. The text recognition rate is 96.9% on the test samples from practical documents with multi-font-types and mixed scripts.</s0>
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<s5>63</s5>
</fC03><fC03 i1="04" i2="3" l="FRE"><s0>Reconnaissance optique caractère</s0>
<s5>64</s5>
</fC03><fC03 i1="04" i2="3" l="ENG"><s0>Optical character recognition</s0>
<s5>64</s5>
</fC03><fC03 i1="05" i2="3" l="FRE"><s0>Jeu caractère</s0>
<s5>65</s5>
</fC03><fC03 i1="05" i2="3" l="ENG"><s0>Character sets</s0>
<s5>65</s5>
</fC03><fC03 i1="06" i2="3" l="FRE"><s0>Système expert</s0>
<s5>66</s5>
</fC03><fC03 i1="06" i2="3" l="ENG"><s0>Expert systems</s0>
<s5>66</s5>
</fC03><fC03 i1="07" i2="3" l="FRE"><s0>Reconnaissance caractère</s0>
<s5>67</s5>
</fC03><fC03 i1="07" i2="3" l="ENG"><s0>Character recognition</s0>
<s5>67</s5>
</fC03><fC03 i1="08" i2="X" l="FRE"><s0>Système n niveaux</s0>
<s5>68</s5>
</fC03><fC03 i1="08" i2="X" l="ENG"><s0>Multilevel system</s0>
<s5>68</s5>
</fC03><fC03 i1="08" i2="X" l="SPA"><s0>Sistema n niveles</s0>
<s5>68</s5>
</fC03><fC03 i1="09" i2="X" l="FRE"><s0>Classification automatique</s0>
<s5>69</s5>
</fC03><fC03 i1="09" i2="X" l="ENG"><s0>Automatic classification</s0>
<s5>69</s5>
</fC03><fC03 i1="09" i2="X" l="SPA"><s0>Clasificación automática</s0>
<s5>69</s5>
</fC03><fC03 i1="10" i2="3" l="FRE"><s0>0130C</s0>
<s4>INC</s4>
<s5>83</s5>
</fC03><fC03 i1="11" i2="3" l="FRE"><s0>Traitement image</s0>
<s4>INC</s4>
<s5>84</s5>
</fC03><fC03 i1="12" i2="3" l="FRE"><s0>4230V</s0>
<s4>INC</s4>
<s5>91</s5>
</fC03><fN21><s1>264</s1>
</fN21><fN44 i1="01"><s1>OTO</s1>
</fN44><fN82><s1>OTO</s1>
</fN82><pR><fA30 i1="01" i2="1" l="ENG"><s1>Document recognition and retrieval</s1>
<s2>16</s2>
<s3>San Jose CA USA</s3>
<s4>2009</s4>
</fA30><server><NO>PASCAL 09-0372245 INIST</NO>
<ET>Multi-font printed Mongolian document recognition system</ET>
<AU>LIANGRUI PENG; CHANGSONG LIU; XIAOQING DING; HUA WANG; JIANMING JIN; BERKNER (Kathrin); LIKFORMAN-SULEM (Laurence)</AU>
<AF>Department of Electronic Engineering, Tsinghua University, Tsinghua National Laboratory for Information Science and Technology State Key Laboratory of Intelligent Technology and Systems/Beijing, 100084/Chine (1 aut., 2 aut., 3 aut., 4 aut., 5 aut.)</AF>
<DT>Publication en série; Congrès; Niveau analytique</DT>
<SO>Proceedings of SPIE, the International Society for Optical Engineering; ISSN 0277-786X; Coden PSISDG; Etats-Unis; Da. 2009; Vol. 7247; 72470J.1-72470J.7; Bibl. 5 ref.</SO>
<LA>Anglais</LA>
<EA>Mongolian is one of the major ethnic languages in China. Large amount of Mongolian printed documents need to be digitized in digital library and various applications. Traditional Mongolian script has unique writing style and multi-font-type variations, which bring challenges to Mongolian OCR research. As traditional Mongolian script has some characteristics, for example, one character may be part of another character, we define the character set for recognition according to the segmented components, and the components are combined into characters by rule-based post-processing module. For character recognition, a method based on visual directional feature and multi-level classifiers is presented. For character segmentation, a scheme is used to find the segmentation point by analyzing the properties of proj ection and connected components. As Mongolian has different font-types which are categorized into two major groups, the parameter of segmentation is adjusted for each group. A font-type classification method for the two font-type group is introduced. For recognition of Mongolian text mixed with Chinese and English, language identification and relevant character recognition kernels are integrated. Experiments show that the presented methods are effective. The text recognition rate is 96.9% on the test samples from practical documents with multi-font-types and mixed scripts.</EA>
<CC>001B00A30C; 001B40B30V</CC>
<FD>Document imprimé; Chine; Bibliothèque électronique; Reconnaissance optique caractère; Jeu caractère; Système expert; Reconnaissance caractère; Système n niveaux; Classification automatique; 0130C; Traitement image; 4230V</FD>
<ED>Printed document; China; Digital libraries; Optical character recognition; Character sets; Expert systems; Character recognition; Multilevel system; Automatic classification</ED>
<SD>Documento impreso; Sistema n niveles; Clasificación automática</SD>
<LO>INIST-21760.354000172953960180</LO>
<ID>09-0372245</ID>
</server>
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