OcrV1, PascalFrancis, Curation, bibRecord, 000555

Morphological preprocessing method to thresholding degraded word images

Identifieur interne : 000555 ( PascalFrancis/Curation ); précédent : 000554; suivant : 000556

Morphological preprocessing method to thresholding degraded word images

Auteurs : Shigueo Nomura [Japon] ; Keiji Yamanaka [Brésil] ; Takayuki Shiose [Japon] ; Hiroshi Kawakami [Japon] ; Osamu Katai [Japon]

Source :

Pattern recognition letters [ 0167-8655 ] ; 2009.

RBID : Pascal:09-0265418

Descripteurs français

Pascal (Inist)
- Détection seuil, Morphologie mathématique, Ombre, Bruit fond, Image binaire, Segmentation, Localisation, Méthode adaptative, Image niveau gris, Filtre morphologique, Reconnaissance optique caractère, Complexité calcul, Appareillage essai, Imageur, Analyse qualitative, Evaluation performance, Reconnaissance forme.
Wicri :
- topic : Analyse qualitative.

English descriptors

KwdEn :
- Adaptive method, Background noise, Binary image, Computational complexity, Grey level image, Imager, Localization, Mathematical morphology, Morphological filter, Optical character recognition, Pattern recognition, Performance evaluation, Qualitative analysis, Segmentation, Shadow, Testing equipment, Threshold detection.

Abstract

This paper presents a novel preprocessing method based on mathematical morphology techniques to improve the subsequent thresholding quality of raw degraded word images. The raw degraded word images contain undesirable shapes called critical shadows on the background that cause noise in binary images. This noise constitutes obstacles to posterior segmentation of characters. Direct application of a thresholding method produces inadequate binary versions of these degraded word images. Our preprocessing method called Shadow Location and Lightening (SL^*L) adaptively, accurately and without manual fine-tuning of parameters locates these critical shadows on grayscale degraded images using morphological operations, and lightens them before applying eventual thresholding process. In this way, enhanced binary images without unpredictable and inappropriate noise can be provided to subsequent segmentation of characters. Then, adequate binary characters can be segmented and extracted as input data to optical character recognition (OCR) applications saving computational effort and increasing recognition rate. The proposed method is experimentally tested with a set of several raw degraded images extracted from real photos acquired by unsophisticated imaging systems. A qualitative analysis of experimental results led to conclusions that the thresholding result quality was significantly improved with the proposed preprocessing method. Also, a quantitative evaluation using a testing data of 1194 degraded word images showed the essentiality and effectiveness of the proposed preprocessing method to increase segmentation and recognition rates of their characters. Furthermore, an advantage of the proposed method is that Otsu's method as a simple and easily implementable global thresholding technique can be sufficient to reducing computational load.

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A08	`01`	`1`	`ENG`	`@1 Morphological preprocessing method to thresholding degraded word images`
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A11	`02`	`1`		`@1 YAMANAKA (Keiji)`
A11	`03`	`1`		`@1 SHIOSE (Takayuki)`
A11	`04`	`1`		`@1 KAWAKAMI (Hiroshi)`
A11	`05`	`1`		`@1 KATAI (Osamu)`
A14	`01`			`@1 Department of Systems Science, Graduate School of Informatics, Kyoto University, Yoshida Honmachi @2 Sakyo-ku, Kyoto 606-8501 @3 JPN @Z 1 aut. @Z 3 aut. @Z 4 aut. @Z 5 aut.`
A14	`02`			`@1 Faculty of Electrical Engineering, Federal University of Uberlândia @2 Uberlândia 38400-902 @3 BRA @Z 2 aut.`
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C01	`01`		`ENG`	@0 This paper presents a novel preprocessing method based on mathematical morphology techniques to improve the subsequent thresholding quality of raw degraded word images. The raw degraded word images contain undesirable shapes called critical shadows on the background that cause noise in binary images. This noise constitutes obstacles to posterior segmentation of characters. Direct application of a thresholding method produces inadequate binary versions of these degraded word images. Our preprocessing method called Shadow Location and Lightening (SL^*L) adaptively, accurately and without manual fine-tuning of parameters locates these critical shadows on grayscale degraded images using morphological operations, and lightens them before applying eventual thresholding process. In this way, enhanced binary images without unpredictable and inappropriate noise can be provided to subsequent segmentation of characters. Then, adequate binary characters can be segmented and extracted as input data to optical character recognition (OCR) applications saving computational effort and increasing recognition rate. The proposed method is experimentally tested with a set of several raw degraded images extracted from real photos acquired by unsophisticated imaging systems. A qualitative analysis of experimental results led to conclusions that the thresholding result quality was significantly improved with the proposed preprocessing method. Also, a quantitative evaluation using a testing data of 1194 degraded word images showed the essentiality and effectiveness of the proposed preprocessing method to increase segmentation and recognition rates of their characters. Furthermore, an advantage of the proposed method is that Otsu's method as a simple and easily implementable global thresholding technique can be sufficient to reducing computational load.
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C03	`07`	`X`	`SPA`	`@0 Localización @5 07`
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C03	`10`	`X`	`ENG`	`@0 Morphological filter @5 10`
C03	`10`	`X`	`SPA`	`@0 Filtro morfológico @5 10`
C03	`11`	`X`	`FRE`	`@0 Reconnaissance optique caractère @5 11`
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C03	`17`	`X`	`SPA`	`@0 Reconocimiento patrón @5 31`
N21				`@1 195`
N44	`01`			`@1 OTO`
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Le document en format XML

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<front><div type="abstract" xml:lang="en">This paper presents a novel preprocessing method based on mathematical morphology techniques to improve the subsequent thresholding quality of raw degraded word images. The raw degraded word images contain undesirable shapes called critical shadows on the background that cause noise in binary images. This noise constitutes obstacles to posterior segmentation of characters. Direct application of a thresholding method produces inadequate binary versions of these degraded word images. Our preprocessing method called Shadow Location and Lightening (SL<sup>*</sup>
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<fC01 i1="01" l="ENG"><s0>This paper presents a novel preprocessing method based on mathematical morphology techniques to improve the subsequent thresholding quality of raw degraded word images. The raw degraded word images contain undesirable shapes called critical shadows on the background that cause noise in binary images. This noise constitutes obstacles to posterior segmentation of characters. Direct application of a thresholding method produces inadequate binary versions of these degraded word images. Our preprocessing method called Shadow Location and Lightening (SL<sup>*</sup>
L) adaptively, accurately and without manual fine-tuning of parameters locates these critical shadows on grayscale degraded images using morphological operations, and lightens them before applying eventual thresholding process. In this way, enhanced binary images without unpredictable and inappropriate noise can be provided to subsequent segmentation of characters. Then, adequate binary characters can be segmented and extracted as input data to optical character recognition (OCR) applications saving computational effort and increasing recognition rate. The proposed method is experimentally tested with a set of several raw degraded images extracted from real photos acquired by unsophisticated imaging systems. A qualitative analysis of experimental results led to conclusions that the thresholding result quality was significantly improved with the proposed preprocessing method. Also, a quantitative evaluation using a testing data of 1194 degraded word images showed the essentiality and effectiveness of the proposed preprocessing method to increase segmentation and recognition rates of their characters. Furthermore, an advantage of the proposed method is that Otsu's method as a simple and easily implementable global thresholding technique can be sufficient to reducing computational load.</s0>
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<fC03 i1="03" i2="X" l="SPA"><s0>Sombra</s0>
<s5>03</s5>
</fC03>
<fC03 i1="04" i2="X" l="FRE"><s0>Bruit fond</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="X" l="ENG"><s0>Background noise</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="X" l="SPA"><s0>Ruido fondo</s0>
<s5>04</s5>
</fC03>
<fC03 i1="05" i2="X" l="FRE"><s0>Image binaire</s0>
<s5>05</s5>
</fC03>
<fC03 i1="05" i2="X" l="ENG"><s0>Binary image</s0>
<s5>05</s5>
</fC03>
<fC03 i1="05" i2="X" l="SPA"><s0>Imagen binaria</s0>
<s5>05</s5>
</fC03>
<fC03 i1="06" i2="X" l="FRE"><s0>Segmentation</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="X" l="ENG"><s0>Segmentation</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="X" l="SPA"><s0>Segmentación</s0>
<s5>06</s5>
</fC03>
<fC03 i1="07" i2="X" l="FRE"><s0>Localisation</s0>
<s5>07</s5>
</fC03>
<fC03 i1="07" i2="X" l="ENG"><s0>Localization</s0>
<s5>07</s5>
</fC03>
<fC03 i1="07" i2="X" l="SPA"><s0>Localización</s0>
<s5>07</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Méthode adaptative</s0>
<s5>08</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Adaptive method</s0>
<s5>08</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Método adaptativo</s0>
<s5>08</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE"><s0>Image niveau gris</s0>
<s5>09</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG"><s0>Grey level image</s0>
<s5>09</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Imagen nivel gris</s0>
<s5>09</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE"><s0>Filtre morphologique</s0>
<s5>10</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG"><s0>Morphological filter</s0>
<s5>10</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA"><s0>Filtro morfológico</s0>
<s5>10</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE"><s0>Reconnaissance optique caractère</s0>
<s5>11</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG"><s0>Optical character recognition</s0>
<s5>11</s5>
</fC03>
<fC03 i1="11" i2="X" l="SPA"><s0>Reconocimento óptico de caracteres</s0>
<s5>11</s5>
</fC03>
<fC03 i1="12" i2="X" l="FRE"><s0>Complexité calcul</s0>
<s5>12</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG"><s0>Computational complexity</s0>
<s5>12</s5>
</fC03>
<fC03 i1="12" i2="X" l="SPA"><s0>Complejidad computación</s0>
<s5>12</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE"><s0>Appareillage essai</s0>
<s5>13</s5>
</fC03>
<fC03 i1="13" i2="X" l="ENG"><s0>Testing equipment</s0>
<s5>13</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA"><s0>Aparato ensayo</s0>
<s5>13</s5>
</fC03>
<fC03 i1="14" i2="X" l="FRE"><s0>Imageur</s0>
<s5>14</s5>
</fC03>
<fC03 i1="14" i2="X" l="ENG"><s0>Imager</s0>
<s5>14</s5>
</fC03>
<fC03 i1="14" i2="X" l="SPA"><s0>Imager</s0>
<s5>14</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE"><s0>Analyse qualitative</s0>
<s5>15</s5>
</fC03>
<fC03 i1="15" i2="X" l="ENG"><s0>Qualitative analysis</s0>
<s5>15</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA"><s0>Análisis cualitativo</s0>
<s5>15</s5>
</fC03>
<fC03 i1="16" i2="X" l="FRE"><s0>Evaluation performance</s0>
<s5>16</s5>
</fC03>
<fC03 i1="16" i2="X" l="ENG"><s0>Performance evaluation</s0>
<s5>16</s5>
</fC03>
<fC03 i1="16" i2="X" l="SPA"><s0>Evaluación prestación</s0>
<s5>16</s5>
</fC03>
<fC03 i1="17" i2="X" l="FRE"><s0>Reconnaissance forme</s0>
<s5>31</s5>
</fC03>
<fC03 i1="17" i2="X" l="ENG"><s0>Pattern recognition</s0>
<s5>31</s5>
</fC03>
<fC03 i1="17" i2="X" l="SPA"><s0>Reconocimiento patrón</s0>
<s5>31</s5>
</fC03>
<fN21><s1>195</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
</standard>
</inist>
</record>

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Morphological preprocessing method to thresholding degraded word images

Morphological preprocessing method to thresholding degraded word images

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