Improved Document Image Segmentation Algorithm using Multiresolution Morphology
Identifieur interne :
000136 ( PascalFrancis/Corpus );
précédent :
000135;
suivant :
000137
Improved Document Image Segmentation Algorithm using Multiresolution Morphology
Auteurs : SYED SAQIB BUKHARI ;
Faisal Shafait ;
Thomas M. BreuelSource :
-
Proceedings of SPIE, the International Society for Optical Engineering [ 0277-786X ] ; 2011.
RBID : Pascal:11-0278984
Descripteurs français
- Pascal (Inist)
- Imagerie,
Algorithme,
Segmentation image,
Traitement image,
Traitement image document,
Analyse multirésolution,
Reconnaissance optique caractère,
Précision,
Marché concurrentiel,
0130C,
4230,
0705P,
4230V.
English descriptors
Abstract
Page segmentation into text and non-text elements is an essential preprocessing step before optical character recognition (OCR) operation. In case of poor segmentation, an OCR classification engine produces garbage characters due to the presence of non-text elements. This paper describes modifications to the text/non-text segmentation algorithm presented by Bloomberg,l which is also available in his open-source Leptonica library.2 The modifications result in significant improvements and achieved better segmentation accuracy than the original algorithm for UW-III, UNLV, ICDAR 2009 page segmentation competition test images and circuit diagram datasets.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
| pA |
| A01 | 01 | 1 | | @0 0277-786X |
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| A02 | 01 | | | @0 PSISDG |
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| A03 | | 1 | | @0 Proc. SPIE Int. Soc. Opt. Eng. |
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| A05 | | | | @2 7874 |
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| A08 | 01 | 1 | ENG | @1 Improved Document Image Segmentation Algorithm using Multiresolution Morphology |
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| A09 | 01 | 1 | ENG | @1 DOcument recognition and retrieval XVIII : 26-27 January 2011, San Francisco, California, United States |
|---|
| A11 | 01 | 1 | | @1 SYED SAQIB BUKHARI |
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| A11 | 02 | 1 | | @1 SHAFAIT (Faisal) |
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| A11 | 03 | 1 | | @1 BREUEL (Thomas M.) |
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| A12 | 01 | 1 | | @1 AGAM (Gady) @9 ed. |
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| A12 | 02 | 1 | | @1 VIARD-GAUDIN (Christian) @9 ed. |
|---|
| A14 | 01 | | | @1 Technical University of Kaiserslautern @2 Kaiserslautern @3 DEU @Z 1 aut. @Z 3 aut. |
|---|
| A14 | 02 | | | @1 German Research Center for Artificial Intelligence (DFKI) @2 Kaiserslautern @3 DEU @Z 2 aut. |
|---|
| A18 | 01 | 1 | | @1 SPIE @3 USA @9 org-cong. |
|---|
| A20 | | | | @2 78740D.1-78740D.8 |
|---|
| A21 | | | | @1 2011 |
|---|
| A23 | 01 | | | @0 ENG |
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| A25 | 01 | | | @1 SPIE @2 Bellingham WA |
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| A26 | 01 | | | @0 978-0-8194-8411-6 |
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| A43 | 01 | | | @1 INIST @2 21760 @5 354000174732580120 |
|---|
| A44 | | | | @0 0000 @1 © 2011 INIST-CNRS. All rights reserved. |
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| A45 | | | | @0 10 ref. |
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| A47 | 01 | 1 | | @0 11-0278984 |
|---|
| A60 | | | | @1 P @2 C |
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| A61 | | | | @0 A |
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| A64 | 01 | 1 | | @0 Proceedings of SPIE, the International Society for Optical Engineering |
|---|
| A66 | 01 | | | @0 USA |
|---|
| C01 | 01 | | ENG | @0 Page segmentation into text and non-text elements is an essential preprocessing step before optical character recognition (OCR) operation. In case of poor segmentation, an OCR classification engine produces garbage characters due to the presence of non-text elements. This paper describes modifications to the text/non-text segmentation algorithm presented by Bloomberg,l which is also available in his open-source Leptonica library.2 The modifications result in significant improvements and achieved better segmentation accuracy than the original algorithm for UW-III, UNLV, ICDAR 2009 page segmentation competition test images and circuit diagram datasets. |
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| C02 | 03 | 3 | | @0 001B00G05P |
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| C03 | 01 | X | FRE | @0 Imagerie @5 19 |
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| C03 | 01 | X | ENG | @0 Imagery @5 19 |
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| C03 | 01 | X | SPA | @0 Imaginería @5 19 |
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| C03 | 02 | 3 | FRE | @0 Algorithme @5 23 |
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| C03 | 02 | 3 | ENG | @0 Algorithms @5 23 |
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| C03 | 03 | 3 | FRE | @0 Segmentation image @5 61 |
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| C03 | 03 | 3 | ENG | @0 Image segmentation @5 61 |
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| C03 | 04 | 3 | FRE | @0 Traitement image @5 62 |
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| C03 | 04 | 3 | ENG | @0 Image processing @5 62 |
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| C03 | 05 | 3 | FRE | @0 Traitement image document @5 63 |
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| C03 | 05 | 3 | ENG | @0 Document image processing @5 63 |
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| C03 | 06 | X | FRE | @0 Analyse multirésolution @5 64 |
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| C03 | 06 | X | ENG | @0 Multiresolution analysis @5 64 |
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| C03 | 06 | X | SPA | @0 Análisis multiresolución @5 64 |
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| C03 | 07 | 3 | FRE | @0 Reconnaissance optique caractère @5 65 |
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| C03 | 07 | 3 | ENG | @0 Optical character recognition @5 65 |
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| C03 | 08 | 3 | FRE | @0 Précision @5 66 |
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| C03 | 08 | 3 | ENG | @0 Accuracy @5 66 |
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| C03 | 09 | X | FRE | @0 Marché concurrentiel @5 67 |
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| C03 | 09 | X | ENG | @0 Open market @5 67 |
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| C03 | 09 | X | SPA | @0 Libre mercado @5 67 |
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| C03 | 10 | 3 | FRE | @0 0130C @4 INC @5 83 |
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| C03 | 11 | 3 | FRE | @0 4230 @4 INC @5 84 |
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| C03 | 12 | 3 | FRE | @0 0705P @4 INC @5 91 |
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| C03 | 13 | 3 | FRE | @0 4230V @4 INC @5 92 |
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| N21 | | | | @1 185 |
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| N44 | 01 | | | @1 OTO |
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| N82 | | | | @1 OTO |
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| pR |
| A30 | 01 | 1 | ENG | @1 Electronic Imaging Science and Technology Symposium @3 San Francisco CA USA @4 2010 |
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Format Inist (serveur)
| NO : | PASCAL 11-0278984 INIST |
|---|
| ET : | Improved Document Image Segmentation Algorithm using Multiresolution Morphology |
|---|
| AU : | SYED SAQIB BUKHARI; SHAFAIT (Faisal); BREUEL (Thomas M.); AGAM (Gady); VIARD-GAUDIN (Christian) |
|---|
| AF : | Technical University of Kaiserslautern/Kaiserslautern/Allemagne (1 aut., 3 aut.); German Research Center for Artificial Intelligence (DFKI)/Kaiserslautern/Allemagne (2 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. 2011; Vol. 7874; 78740D.1-78740D.8; Bibl. 10 ref. |
|---|
| LA : | Anglais |
|---|
| EA : | Page segmentation into text and non-text elements is an essential preprocessing step before optical character recognition (OCR) operation. In case of poor segmentation, an OCR classification engine produces garbage characters due to the presence of non-text elements. This paper describes modifications to the text/non-text segmentation algorithm presented by Bloomberg,l which is also available in his open-source Leptonica library.2 The modifications result in significant improvements and achieved better segmentation accuracy than the original algorithm for UW-III, UNLV, ICDAR 2009 page segmentation competition test images and circuit diagram datasets. |
|---|
| CC : | 001B00A30C; 001B40B30V; 001B00G05P |
|---|
| FD : | Imagerie; Algorithme; Segmentation image; Traitement image; Traitement image document; Analyse multirésolution; Reconnaissance optique caractère; Précision; Marché concurrentiel; 0130C; 4230; 0705P; 4230V |
|---|
| ED : | Imagery; Algorithms; Image segmentation; Image processing; Document image processing; Multiresolution analysis; Optical character recognition; Accuracy; Open market |
|---|
| SD : | Imaginería; Análisis multiresolución; Libre mercado |
|---|
| LO : | INIST-21760.354000174732580120 |
|---|
| ID : | 11-0278984 |
|---|
Links to Exploration step
Pascal:11-0278984
Le document en format XML
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</fA30><server><NO>PASCAL 11-0278984 INIST</NO>
<ET>Improved Document Image Segmentation Algorithm using Multiresolution Morphology</ET>
<AU>SYED SAQIB BUKHARI; SHAFAIT (Faisal); BREUEL (Thomas M.); AGAM (Gady); VIARD-GAUDIN (Christian)</AU>
<AF>Technical University of Kaiserslautern/Kaiserslautern/Allemagne (1 aut., 3 aut.); German Research Center for Artificial Intelligence (DFKI)/Kaiserslautern/Allemagne (2 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. 2011; Vol. 7874; 78740D.1-78740D.8; Bibl. 10 ref.</SO>
<LA>Anglais</LA>
<EA>Page segmentation into text and non-text elements is an essential preprocessing step before optical character recognition (OCR) operation. In case of poor segmentation, an OCR classification engine produces garbage characters due to the presence of non-text elements. This paper describes modifications to the text/non-text segmentation algorithm presented by Bloomberg,<sup>l</sup>
which is also available in his open-source Leptonica library.<sup>2</sup>
The modifications result in significant improvements and achieved better segmentation accuracy than the original algorithm for UW-III, UNLV, ICDAR 2009 page segmentation competition test images and circuit diagram datasets.</EA><CC>001B00A30C; 001B40B30V; 001B00G05P</CC>
<FD>Imagerie; Algorithme; Segmentation image; Traitement image; Traitement image document; Analyse multirésolution; Reconnaissance optique caractère; Précision; Marché concurrentiel; 0130C; 4230; 0705P; 4230V</FD>
<ED>Imagery; Algorithms; Image segmentation; Image processing; Document image processing; Multiresolution analysis; Optical character recognition; Accuracy; Open market</ED>
<SD>Imaginería; Análisis multiresolución; Libre mercado</SD>
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