Serveur d'exploration sur l'OCR

Attention, ce site est en cours de développement !
Attention, site généré par des moyens informatiques à partir de corpus bruts.
Les informations ne sont donc pas validées.

Text area detection from video frames

Identifieur interne : 000699 ( PascalFrancis/Corpus ); précédent : 000698; suivant : 000700

Text area detection from video frames

Auteurs : XIANGRONG CHEN ; HONGJIANG ZHANG

Source :

RBID : Pascal:02-0008505

Descripteurs français

English descriptors

Abstract

Text area detection from video frame is an essential step for Video OCR. The key problem is the complex background of the video frames. This paper proposes a novel approach to this problem. First, we use the vertical edge information to detect candidate text areas. The horizontal edge information is then used to eliminate some of the false candidates. Finally, shape suppression technique is applied to further refine the results. Experimental results have shown the proposed approach is very effective in text area detection.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

pA  
A01 01  1    @0 0302-9743
A05       @2 2195
A08 01  1  ENG  @1 Text area detection from video frames
A09 01  1  ENG  @1 Advances in multimedia information processing - PCM 2001 : Beijing, 24-26 October 2001
A11 01  1    @1 XIANGRONG CHEN
A11 02  1    @1 HONGJIANG ZHANG
A12 01  1    @1 HEUNG-YEUNG SHUM @9 ed.
A12 02  1    @1 LIAO (Mark) @9 ed.
A12 03  1    @1 SHIH-FU CHANG @9 ed.
A14 01      @1 Microsoft Research China @3 CHN @Z 1 aut. @Z 2 aut.
A20       @1 222-228
A21       @1 2001
A23 01      @0 ENG
A26 01      @0 3-540-42680-9
A43 01      @1 INIST @2 16343 @5 354000097042980290
A44       @0 0000 @1 © 2002 INIST-CNRS. All rights reserved.
A45       @0 5 ref.
A47 01  1    @0 02-0008505
A60       @1 P @2 C
A61       @0 A
A64 01  1    @0 Lecture notes in computer science
A66 01      @0 DEU
A66 02      @0 USA
C01 01    ENG  @0 Text area detection from video frame is an essential step for Video OCR. The key problem is the complex background of the video frames. This paper proposes a novel approach to this problem. First, we use the vertical edge information to detect candidate text areas. The horizontal edge information is then used to eliminate some of the false candidates. Finally, shape suppression technique is applied to further refine the results. Experimental results have shown the proposed approach is very effective in text area detection.
C02 01  X    @0 001D02C03
C02 02  X    @0 001D03I01
C03 01  X  FRE  @0 Détection contour @5 01
C03 01  X  ENG  @0 Edge detection @5 01
C03 01  X  SPA  @0 Detección contorno @5 01
C03 02  X  FRE  @0 Texte @5 02
C03 02  X  ENG  @0 Text @5 02
C03 02  X  SPA  @0 Texto @5 02
C03 03  X  FRE  @0 Technique vidéo @5 03
C03 03  X  ENG  @0 Video technique @5 03
C03 03  X  SPA  @0 Técnica video @5 03
C03 04  X  FRE  @0 Video frame @4 INC @5 82
C03 05  X  FRE  @0 Text area detection @4 INC @5 83
N21       @1 001
pR  
A30 01  1  ENG  @1 IEEE Pacific RIm conference on multimedia @2 2 @3 Beijing CHN @4 2001-10-24

Format Inist (serveur)

NO : PASCAL 02-0008505 INIST
ET : Text area detection from video frames
AU : XIANGRONG CHEN; HONGJIANG ZHANG; HEUNG-YEUNG SHUM; LIAO (Mark); SHIH-FU CHANG
AF : Microsoft Research China/Chine (1 aut., 2 aut.)
DT : Publication en série; Congrès; Niveau analytique
SO : Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 2001; Vol. 2195; Pp. 222-228; Bibl. 5 ref.
LA : Anglais
EA : Text area detection from video frame is an essential step for Video OCR. The key problem is the complex background of the video frames. This paper proposes a novel approach to this problem. First, we use the vertical edge information to detect candidate text areas. The horizontal edge information is then used to eliminate some of the false candidates. Finally, shape suppression technique is applied to further refine the results. Experimental results have shown the proposed approach is very effective in text area detection.
CC : 001D02C03; 001D03I01
FD : Détection contour; Texte; Technique vidéo; Video frame; Text area detection
ED : Edge detection; Text; Video technique
SD : Detección contorno; Texto; Técnica video
LO : INIST-16343.354000097042980290
ID : 02-0008505

Links to Exploration step

Pascal:02-0008505

Le document en format XML

<record>
<TEI>
<teiHeader>
<fileDesc>
<titleStmt>
<title xml:lang="en" level="a">Text area detection from video frames</title>
<author>
<name sortKey="Xiangrong Chen" sort="Xiangrong Chen" uniqKey="Xiangrong Chen" last="Xiangrong Chen">XIANGRONG CHEN</name>
<affiliation>
<inist:fA14 i1="01">
<s1>Microsoft Research China</s1>
<s3>CHN</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
<author>
<name sortKey="Hongjiang Zhang" sort="Hongjiang Zhang" uniqKey="Hongjiang Zhang" last="Hongjiang Zhang">HONGJIANG ZHANG</name>
<affiliation>
<inist:fA14 i1="01">
<s1>Microsoft Research China</s1>
<s3>CHN</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
</titleStmt>
<publicationStmt>
<idno type="wicri:source">INIST</idno>
<idno type="inist">02-0008505</idno>
<date when="2001">2001</date>
<idno type="stanalyst">PASCAL 02-0008505 INIST</idno>
<idno type="RBID">Pascal:02-0008505</idno>
<idno type="wicri:Area/PascalFrancis/Corpus">000699</idno>
</publicationStmt>
<sourceDesc>
<biblStruct>
<analytic>
<title xml:lang="en" level="a">Text area detection from video frames</title>
<author>
<name sortKey="Xiangrong Chen" sort="Xiangrong Chen" uniqKey="Xiangrong Chen" last="Xiangrong Chen">XIANGRONG CHEN</name>
<affiliation>
<inist:fA14 i1="01">
<s1>Microsoft Research China</s1>
<s3>CHN</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
<author>
<name sortKey="Hongjiang Zhang" sort="Hongjiang Zhang" uniqKey="Hongjiang Zhang" last="Hongjiang Zhang">HONGJIANG ZHANG</name>
<affiliation>
<inist:fA14 i1="01">
<s1>Microsoft Research China</s1>
<s3>CHN</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
</affiliation>
</author>
</analytic>
<series>
<title level="j" type="main">Lecture notes in computer science</title>
<idno type="ISSN">0302-9743</idno>
<imprint>
<date when="2001">2001</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
<seriesStmt>
<title level="j" type="main">Lecture notes in computer science</title>
<idno type="ISSN">0302-9743</idno>
</seriesStmt>
</fileDesc>
<profileDesc>
<textClass>
<keywords scheme="KwdEn" xml:lang="en">
<term>Edge detection</term>
<term>Text</term>
<term>Video technique</term>
</keywords>
<keywords scheme="Pascal" xml:lang="fr">
<term>Détection contour</term>
<term>Texte</term>
<term>Technique vidéo</term>
<term>Video frame</term>
<term>Text area detection</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front>
<div type="abstract" xml:lang="en">Text area detection from video frame is an essential step for Video OCR. The key problem is the complex background of the video frames. This paper proposes a novel approach to this problem. First, we use the vertical edge information to detect candidate text areas. The horizontal edge information is then used to eliminate some of the false candidates. Finally, shape suppression technique is applied to further refine the results. Experimental results have shown the proposed approach is very effective in text area detection.</div>
</front>
</TEI>
<inist>
<standard h6="B">
<pA>
<fA01 i1="01" i2="1">
<s0>0302-9743</s0>
</fA01>
<fA05>
<s2>2195</s2>
</fA05>
<fA08 i1="01" i2="1" l="ENG">
<s1>Text area detection from video frames</s1>
</fA08>
<fA09 i1="01" i2="1" l="ENG">
<s1>Advances in multimedia information processing - PCM 2001 : Beijing, 24-26 October 2001</s1>
</fA09>
<fA11 i1="01" i2="1">
<s1>XIANGRONG CHEN</s1>
</fA11>
<fA11 i1="02" i2="1">
<s1>HONGJIANG ZHANG</s1>
</fA11>
<fA12 i1="01" i2="1">
<s1>HEUNG-YEUNG SHUM</s1>
<s9>ed.</s9>
</fA12>
<fA12 i1="02" i2="1">
<s1>LIAO (Mark)</s1>
<s9>ed.</s9>
</fA12>
<fA12 i1="03" i2="1">
<s1>SHIH-FU CHANG</s1>
<s9>ed.</s9>
</fA12>
<fA14 i1="01">
<s1>Microsoft Research China</s1>
<s3>CHN</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</fA14>
<fA20>
<s1>222-228</s1>
</fA20>
<fA21>
<s1>2001</s1>
</fA21>
<fA23 i1="01">
<s0>ENG</s0>
</fA23>
<fA26 i1="01">
<s0>3-540-42680-9</s0>
</fA26>
<fA43 i1="01">
<s1>INIST</s1>
<s2>16343</s2>
<s5>354000097042980290</s5>
</fA43>
<fA44>
<s0>0000</s0>
<s1>© 2002 INIST-CNRS. All rights reserved.</s1>
</fA44>
<fA45>
<s0>5 ref.</s0>
</fA45>
<fA47 i1="01" i2="1">
<s0>02-0008505</s0>
</fA47>
<fA60>
<s1>P</s1>
<s2>C</s2>
</fA60>
<fA61>
<s0>A</s0>
</fA61>
<fA64 i1="01" i2="1">
<s0>Lecture notes in computer science</s0>
</fA64>
<fA66 i1="01">
<s0>DEU</s0>
</fA66>
<fA66 i1="02">
<s0>USA</s0>
</fA66>
<fC01 i1="01" l="ENG">
<s0>Text area detection from video frame is an essential step for Video OCR. The key problem is the complex background of the video frames. This paper proposes a novel approach to this problem. First, we use the vertical edge information to detect candidate text areas. The horizontal edge information is then used to eliminate some of the false candidates. Finally, shape suppression technique is applied to further refine the results. Experimental results have shown the proposed approach is very effective in text area detection.</s0>
</fC01>
<fC02 i1="01" i2="X">
<s0>001D02C03</s0>
</fC02>
<fC02 i1="02" i2="X">
<s0>001D03I01</s0>
</fC02>
<fC03 i1="01" i2="X" l="FRE">
<s0>Détection contour</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="ENG">
<s0>Edge detection</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="SPA">
<s0>Detección contorno</s0>
<s5>01</s5>
</fC03>
<fC03 i1="02" i2="X" l="FRE">
<s0>Texte</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="ENG">
<s0>Text</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="SPA">
<s0>Texto</s0>
<s5>02</s5>
</fC03>
<fC03 i1="03" i2="X" l="FRE">
<s0>Technique vidéo</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="X" l="ENG">
<s0>Video technique</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="X" l="SPA">
<s0>Técnica video</s0>
<s5>03</s5>
</fC03>
<fC03 i1="04" i2="X" l="FRE">
<s0>Video frame</s0>
<s4>INC</s4>
<s5>82</s5>
</fC03>
<fC03 i1="05" i2="X" l="FRE">
<s0>Text area detection</s0>
<s4>INC</s4>
<s5>83</s5>
</fC03>
<fN21>
<s1>001</s1>
</fN21>
</pA>
<pR>
<fA30 i1="01" i2="1" l="ENG">
<s1>IEEE Pacific RIm conference on multimedia</s1>
<s2>2</s2>
<s3>Beijing CHN</s3>
<s4>2001-10-24</s4>
</fA30>
</pR>
</standard>
<server>
<NO>PASCAL 02-0008505 INIST</NO>
<ET>Text area detection from video frames</ET>
<AU>XIANGRONG CHEN; HONGJIANG ZHANG; HEUNG-YEUNG SHUM; LIAO (Mark); SHIH-FU CHANG</AU>
<AF>Microsoft Research China/Chine (1 aut., 2 aut.)</AF>
<DT>Publication en série; Congrès; Niveau analytique</DT>
<SO>Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 2001; Vol. 2195; Pp. 222-228; Bibl. 5 ref.</SO>
<LA>Anglais</LA>
<EA>Text area detection from video frame is an essential step for Video OCR. The key problem is the complex background of the video frames. This paper proposes a novel approach to this problem. First, we use the vertical edge information to detect candidate text areas. The horizontal edge information is then used to eliminate some of the false candidates. Finally, shape suppression technique is applied to further refine the results. Experimental results have shown the proposed approach is very effective in text area detection.</EA>
<CC>001D02C03; 001D03I01</CC>
<FD>Détection contour; Texte; Technique vidéo; Video frame; Text area detection</FD>
<ED>Edge detection; Text; Video technique</ED>
<SD>Detección contorno; Texto; Técnica video</SD>
<LO>INIST-16343.354000097042980290</LO>
<ID>02-0008505</ID>
</server>
</inist>
</record>

Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Ticri/CIDE/explor/OcrV1/Data/PascalFrancis/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000699 | SxmlIndent | more

Ou

HfdSelect -h $EXPLOR_AREA/Data/PascalFrancis/Corpus/biblio.hfd -nk 000699 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Ticri/CIDE
   |area=    OcrV1
   |flux=    PascalFrancis
   |étape=   Corpus
   |type=    RBID
   |clé=     Pascal:02-0008505
   |texte=   Text area detection from video frames
}}

Wicri

This area was generated with Dilib version V0.6.32.
Data generation: Sat Nov 11 16:53:45 2017. Site generation: Mon Mar 11 23:15:16 2024