OcrV1, PascalFrancis, Curation, bibRecord, 000587

Farsi and Arabic document images lossy compression based on the mixed raster content model

Identifieur interne : 000587 ( PascalFrancis/Curation ); précédent : 000586; suivant : 000588

Farsi and Arabic document images lossy compression based on the mixed raster content model

Auteurs : Hadi Grailu [Iran] ; Mojtaba Lotfizad [Iran] ; Hadi Sadoghi-Yazdi [Iran]

Source :

International journal on document analysis and recognition : (Print) [ 1433-2833 ] ; 2009.

RBID : Pascal:10-0182404

Descripteurs français

Pascal (Inist)
- Compression donnée, Compression image, Texte, Reconnaissance caractère, Reconnaissance optique caractère, Concordance forme, Traitement document, Arabe, Trame, Composé modèle, Théorie vitesse distorsion, Lisibilité, Modèle mixte, Modélisation, Segmentation, Optimisation, Artefact, Compression signal, Masque.

English descriptors

KwdEn :
- Arabic, Artefact, Character recognition, Data compression, Document processing, Image compression, Legibility, Mask, Mixed model, Model compound, Modeling, Optical character recognition, Optimization, Pattern matching, Raster, Rate distortion theory, Segmentation, Signal compression, Text.

Abstract

Recently, the mixed raster content model was proposed for compound document image compression. Most state-of-the-art document image compression methods, such as DjVu, work on the basis of this model but they have some disadvantages, especially for Farsi and Arabic document images. First, the Farsi/Arabic script has some characteristics which can be used to further improve the compression performance. Second, existing segmentation methods have focused on well-separating the textual objects from the background and/or optimizing the rate-distortion trade-off; nevertheless, they have not considered the text readability and OCR facility. Third, these methods usually suffer from the undesired jaggy artifact and misclassifying the important textual details. In this paper, MRC-based document image compression method is proposed which compromises rate-distortion trade-off better than the existing state-of-the-art document compression methods. The proposed method has higher performance in the aspects of segmentation, bi-level mask layer compression, OCR facility, and the overall compression. It uses a 1D pattern matching technique for compression of masklayer. It also uses a segmentation method which is sensitive enough to the small textual objects. Experimental results show that the proposed method has considerably higher compression performance than that of the state-of-the-art compression method DjVu, as high as 1.75-2.3.

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A03		`1`		`@0 Int. j. doc. anal. recognit. : (Print)`
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A06				`@2 4`
A08	`01`	`1`	`ENG`	`@1 Farsi and Arabic document images lossy compression based on the mixed raster content model`
A11	`01`	`1`		`@1 GRAILU (Hadi)`
A11	`02`	`1`		`@1 LOTFIZAD (Mojtaba)`
A11	`03`	`1`		`@1 SADOGHI-YAZDI (Hadi)`
A14	`01`			`@1 Department of Electrical Engineering, Tarbiat Modares University @2 Tehran @3 IRN @Z 1 aut. @Z 2 aut.`
A14	`02`			`@1 Department of Computer Engineering, Ferdowsi University of Mashhad @2 Mashhad @3 IRN @Z 3 aut.`
A20				`@1 227-248`
A21				`@1 2009`
A23	`01`			`@0 ENG`
A43	`01`			`@1 INIST @2 26790 @5 354000171651020010`
A44				`@0 0000 @1 © 2010 INIST-CNRS. All rights reserved.`
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A64	`01`	`1`		`@0 International journal on document analysis and recognition : (Print)`
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C01	`01`		`ENG`	@0 Recently, the mixed raster content model was proposed for compound document image compression. Most state-of-the-art document image compression methods, such as DjVu, work on the basis of this model but they have some disadvantages, especially for Farsi and Arabic document images. First, the Farsi/Arabic script has some characteristics which can be used to further improve the compression performance. Second, existing segmentation methods have focused on well-separating the textual objects from the background and/or optimizing the rate-distortion trade-off; nevertheless, they have not considered the text readability and OCR facility. Third, these methods usually suffer from the undesired jaggy artifact and misclassifying the important textual details. In this paper, MRC-based document image compression method is proposed which compromises rate-distortion trade-off better than the existing state-of-the-art document compression methods. The proposed method has higher performance in the aspects of segmentation, bi-level mask layer compression, OCR facility, and the overall compression. It uses a 1D pattern matching technique for compression of masklayer. It also uses a segmentation method which is sensitive enough to the small textual objects. Experimental results show that the proposed method has considerably higher compression performance than that of the state-of-the-art compression method DjVu, as high as 1.75-2.3.
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C03	`03`	`X`	`ENG`	`@0 Text @5 08`
C03	`03`	`X`	`SPA`	`@0 Texto @5 08`
C03	`04`	`X`	`FRE`	`@0 Reconnaissance caractère @5 09`
C03	`04`	`X`	`ENG`	`@0 Character recognition @5 09`
C03	`04`	`X`	`SPA`	`@0 Reconocimiento carácter @5 09`
C03	`05`	`X`	`FRE`	`@0 Reconnaissance optique caractère @5 10`
C03	`05`	`X`	`ENG`	`@0 Optical character recognition @5 10`
C03	`05`	`X`	`SPA`	`@0 Reconocimento óptico de caracteres @5 10`
C03	`06`	`X`	`FRE`	`@0 Concordance forme @5 11`
C03	`06`	`X`	`ENG`	`@0 Pattern matching @5 11`
C03	`07`	`X`	`FRE`	`@0 Traitement document @5 12`
C03	`07`	`X`	`ENG`	`@0 Document processing @5 12`
C03	`07`	`X`	`SPA`	`@0 Tratamiento documento @5 12`
C03	`08`	`X`	`FRE`	`@0 Arabe @5 18`
C03	`08`	`X`	`ENG`	`@0 Arabic @5 18`
C03	`08`	`X`	`SPA`	`@0 Árabe @5 18`
C03	`09`	`X`	`FRE`	`@0 Trame @5 19`
C03	`09`	`X`	`ENG`	`@0 Raster @5 19`
C03	`09`	`X`	`SPA`	`@0 Trama @5 19`
C03	`10`	`X`	`FRE`	`@0 Composé modèle @5 20`
C03	`10`	`X`	`ENG`	`@0 Model compound @5 20`
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C03	`11`	`3`	`FRE`	`@0 Théorie vitesse distorsion @5 21`
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C03	`13`	`X`	`FRE`	`@0 Modèle mixte @5 23`
C03	`13`	`X`	`ENG`	`@0 Mixed model @5 23`
C03	`13`	`X`	`SPA`	`@0 Modelo mixto @5 23`
C03	`14`	`X`	`FRE`	`@0 Modélisation @5 24`
C03	`14`	`X`	`ENG`	`@0 Modeling @5 24`
C03	`14`	`X`	`SPA`	`@0 Modelización @5 24`
C03	`15`	`X`	`FRE`	`@0 Segmentation @5 25`
C03	`15`	`X`	`ENG`	`@0 Segmentation @5 25`
C03	`15`	`X`	`SPA`	`@0 Segmentación @5 25`
C03	`16`	`X`	`FRE`	`@0 Optimisation @5 26`
C03	`16`	`X`	`ENG`	`@0 Optimization @5 26`
C03	`16`	`X`	`SPA`	`@0 Optimización @5 26`
C03	`17`	`X`	`FRE`	`@0 Artefact @5 27`
C03	`17`	`X`	`ENG`	`@0 Artefact @5 27`
C03	`17`	`X`	`SPA`	`@0 Artefacto @5 27`
C03	`18`	`X`	`FRE`	`@0 Compression signal @5 28`
C03	`18`	`X`	`ENG`	`@0 Signal compression @5 28`
C03	`18`	`X`	`SPA`	`@0 Compresión señal @5 28`
C03	`19`	`X`	`FRE`	`@0 Masque @5 41`
C03	`19`	`X`	`ENG`	`@0 Mask @5 41`
C03	`19`	`X`	`SPA`	`@0 Máscara @5 41`
N21				`@1 123`
N44	`01`			`@1 OTO`
N82				`@1 OTO`

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Le document en format XML

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<front><div type="abstract" xml:lang="en">Recently, the mixed raster content model was proposed for compound document image compression. Most state-of-the-art document image compression methods, such as DjVu, work on the basis of this model but they have some disadvantages, especially for Farsi and Arabic document images. First, the Farsi/Arabic script has some characteristics which can be used to further improve the compression performance. Second, existing segmentation methods have focused on well-separating the textual objects from the background and/or optimizing the rate-distortion trade-off; nevertheless, they have not considered the text readability and OCR facility. Third, these methods usually suffer from the undesired jaggy artifact and misclassifying the important textual details. In this paper, MRC-based document image compression method is proposed which compromises rate-distortion trade-off better than the existing state-of-the-art document compression methods. The proposed method has higher performance in the aspects of segmentation, bi-level mask layer compression, OCR facility, and the overall compression. It uses a 1D pattern matching technique for compression of masklayer. It also uses a segmentation method which is sensitive enough to the small textual objects. Experimental results show that the proposed method has considerably higher compression performance than that of the state-of-the-art compression method DjVu, as high as 1.75-2.3.</div>
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<fC01 i1="01" l="ENG"><s0>Recently, the mixed raster content model was proposed for compound document image compression. Most state-of-the-art document image compression methods, such as DjVu, work on the basis of this model but they have some disadvantages, especially for Farsi and Arabic document images. First, the Farsi/Arabic script has some characteristics which can be used to further improve the compression performance. Second, existing segmentation methods have focused on well-separating the textual objects from the background and/or optimizing the rate-distortion trade-off; nevertheless, they have not considered the text readability and OCR facility. Third, these methods usually suffer from the undesired jaggy artifact and misclassifying the important textual details. In this paper, MRC-based document image compression method is proposed which compromises rate-distortion trade-off better than the existing state-of-the-art document compression methods. The proposed method has higher performance in the aspects of segmentation, bi-level mask layer compression, OCR facility, and the overall compression. It uses a 1D pattern matching technique for compression of masklayer. It also uses a segmentation method which is sensitive enough to the small textual objects. Experimental results show that the proposed method has considerably higher compression performance than that of the state-of-the-art compression method DjVu, as high as 1.75-2.3.</s0>
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<s5>19</s5>
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<s5>19</s5>
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<s5>19</s5>
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</fC03>
<fC03 i1="12" i2="X" l="FRE"><s0>Lisibilité</s0>
<s5>22</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG"><s0>Legibility</s0>
<s5>22</s5>
</fC03>
<fC03 i1="12" i2="X" l="SPA"><s0>Legibilidad</s0>
<s5>22</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE"><s0>Modèle mixte</s0>
<s5>23</s5>
</fC03>
<fC03 i1="13" i2="X" l="ENG"><s0>Mixed model</s0>
<s5>23</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA"><s0>Modelo mixto</s0>
<s5>23</s5>
</fC03>
<fC03 i1="14" i2="X" l="FRE"><s0>Modélisation</s0>
<s5>24</s5>
</fC03>
<fC03 i1="14" i2="X" l="ENG"><s0>Modeling</s0>
<s5>24</s5>
</fC03>
<fC03 i1="14" i2="X" l="SPA"><s0>Modelización</s0>
<s5>24</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE"><s0>Segmentation</s0>
<s5>25</s5>
</fC03>
<fC03 i1="15" i2="X" l="ENG"><s0>Segmentation</s0>
<s5>25</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA"><s0>Segmentación</s0>
<s5>25</s5>
</fC03>
<fC03 i1="16" i2="X" l="FRE"><s0>Optimisation</s0>
<s5>26</s5>
</fC03>
<fC03 i1="16" i2="X" l="ENG"><s0>Optimization</s0>
<s5>26</s5>
</fC03>
<fC03 i1="16" i2="X" l="SPA"><s0>Optimización</s0>
<s5>26</s5>
</fC03>
<fC03 i1="17" i2="X" l="FRE"><s0>Artefact</s0>
<s5>27</s5>
</fC03>
<fC03 i1="17" i2="X" l="ENG"><s0>Artefact</s0>
<s5>27</s5>
</fC03>
<fC03 i1="17" i2="X" l="SPA"><s0>Artefacto</s0>
<s5>27</s5>
</fC03>
<fC03 i1="18" i2="X" l="FRE"><s0>Compression signal</s0>
<s5>28</s5>
</fC03>
<fC03 i1="18" i2="X" l="ENG"><s0>Signal compression</s0>
<s5>28</s5>
</fC03>
<fC03 i1="18" i2="X" l="SPA"><s0>Compresión señal</s0>
<s5>28</s5>
</fC03>
<fC03 i1="19" i2="X" l="FRE"><s0>Masque</s0>
<s5>41</s5>
</fC03>
<fC03 i1="19" i2="X" l="ENG"><s0>Mask</s0>
<s5>41</s5>
</fC03>
<fC03 i1="19" i2="X" l="SPA"><s0>Máscara</s0>
<s5>41</s5>
</fC03>
<fN21><s1>123</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
</standard>
</inist>
</record>

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{{Explor lien
   |wiki=    Ticri/CIDE
   |area=    OcrV1
   |flux=    PascalFrancis
   |étape=   Curation
   |type=    RBID
   |clé=     Pascal:10-0182404
   |texte=   Farsi and Arabic document images lossy compression based on the mixed raster content model
}}

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Data generation: Sat Nov 11 16:53:45 2017. Site generation: Mon Mar 11 23:15:16 2024

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Farsi and Arabic document images lossy compression based on the mixed raster content model

Farsi and Arabic document images lossy compression based on the mixed raster content model

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Descripteurs français

English descriptors

Abstract

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