OcrV1, PascalFrancis, Curation, bibRecord, 000446

Key-text spotting in documentary videos using Adaboost

Identifieur interne : 000446 ( PascalFrancis/Curation ); précédent : 000445; suivant : 000447

Key-text spotting in documentary videos using Adaboost

Auteurs : M. Lalonde [Canada] ; L. Gagnon [Canada]

Source :

Proceedings of SPIE, the International Society for Optical Engineering [ 0277-786X ] ; 2006.

RBID : Pascal:07-0365691

Descripteurs français

Pascal (Inist)
- Traitement image, 0705P, 4230V.

English descriptors

KwdEn :
- Image processing.

Abstract

This paper presents a method for spotting key-text in videos, based on a cascade of classifiers trained with Adaboost. The video is first reduced to a set of key-frames. Each key-frame is then analyzed for its text content. Text spotting is performed by scanning the image with a variable-size window (to account for scale) within which simple features (mean/variance of grayscale values and x/y derivatives) are extracted in various sub-areas. Training builds classifiers using the most discriminant spatial combinations of features for text detection. The text-spotting module outputs a decision map of the size of the input key-frame showing regions of interest that may contain text suitable for recognition by an OCR system. Performance is measured against a dataset of 147 key-frames extracted from 22 documentary films of the National Film Board (NFB) of Canada. A detection rate of 97% is obtained with relatively few false alarms.

A01	`01`	`1`		`@0 0277-786X`
A05				`@2 6064`
A08	`01`	`1`	`ENG`	`@1 Key-text spotting in documentary videos using Adaboost`
A09	`01`	`1`	`ENG`	`@1 Image processing : algorithms and systems, neural networks, and machine learning : 16-18 January 2006, San Jose, California, USA`
A11	`01`	`1`		`@1 LALONDE (M.)`
A11	`02`	`1`		`@1 GAGNON (L.)`
A12	`01`	`1`		`@1 DOUGHERTY (Edward R.) @9 ed.`
A14	`01`			`@1 R&D Department, Computer Research Institute of Montreal (CRIM), 550 Sherbrooke West, Suite 100 @2 Montreal, QC, H3A 1B9 @3 CAN @Z 1 aut. @Z 2 aut.`
A18	`01`	`1`		`@1 IS&T--The Society for Imaging Science and Technology @3 USA @9 org-cong.`
A18	`02`	`1`		`@1 Society of photo-optical instrumentation engineers @3 USA @9 org-cong.`
A20				`@2 60641N.1-60641N.8`
A21				`@1 2006`
A23	`01`			`@0 ENG`
A26	`01`			`@0 0-8194-6104-0`
A43	`01`			`@1 INIST @2 21760 @5 354000153558040510`
A44				`@0 0000 @1 © 2007 INIST-CNRS. All rights reserved.`
A45				`@0 17 ref.`
A47	`01`	`1`		`@0 07-0365691`
A60				`@1 P @2 C`
A61				`@0 A`
A64	`01`	`1`		`@0 Proceedings of SPIE, the International Society for Optical Engineering`
A66	`01`			`@0 USA`
C01	`01`		`ENG`	@0 This paper presents a method for spotting key-text in videos, based on a cascade of classifiers trained with Adaboost. The video is first reduced to a set of key-frames. Each key-frame is then analyzed for its text content. Text spotting is performed by scanning the image with a variable-size window (to account for scale) within which simple features (mean/variance of grayscale values and x/y derivatives) are extracted in various sub-areas. Training builds classifiers using the most discriminant spatial combinations of features for text detection. The text-spotting module outputs a decision map of the size of the input key-frame showing regions of interest that may contain text suitable for recognition by an OCR system. Performance is measured against a dataset of 147 key-frames extracted from 22 documentary films of the National Film Board (NFB) of Canada. A detection rate of 97% is obtained with relatively few false alarms.
C02	`01`	`3`		`@0 001B00G05P`
C02	`02`	`3`		`@0 001B40B30V`
C03	`01`	`3`	`FRE`	`@0 Traitement image @5 61`
C03	`01`	`3`	`ENG`	`@0 Image processing @5 61`
C03	`02`	`3`	`FRE`	`@0 0705P @4 INC @5 83`
C03	`03`	`3`	`FRE`	`@0 4230V @4 INC @5 91`
N21				`@1 239`
N44	`01`			`@1 OTO`
N82				`@1 OTO`

A30	`01`	`1`	`ENG`	`@1 Image processing @3 USA @4 2006`

Links toward previous steps (curation, corpus...)

to stream PascalFrancis, to step Corpus: Pour aller vers cette notice dans l'étape Curation :000340

Links to Exploration step

Pascal:07-0365691

Le document en format XML

<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" level="a">Key-text spotting in documentary videos using Adaboost</title>
<author><name sortKey="Lalonde, M" sort="Lalonde, M" uniqKey="Lalonde M" first="M." last="Lalonde">M. Lalonde</name>
<affiliation wicri:level="1"><inist:fA14 i1="01"><s1>R&D Department, Computer Research Institute of Montreal (CRIM), 550 Sherbrooke West, Suite 100</s1>
<s2>Montreal, QC, H3A 1B9</s2>
<s3>CAN</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
<country>Canada</country>
</affiliation>
</author>
<author><name sortKey="Gagnon, L" sort="Gagnon, L" uniqKey="Gagnon L" first="L." last="Gagnon">L. Gagnon</name>
<affiliation wicri:level="1"><inist:fA14 i1="01"><s1>R&D Department, Computer Research Institute of Montreal (CRIM), 550 Sherbrooke West, Suite 100</s1>
<s2>Montreal, QC, H3A 1B9</s2>
<s3>CAN</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
<country>Canada</country>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">INIST</idno>
<idno type="inist">07-0365691</idno>
<date when="2006">2006</date>
<idno type="stanalyst">PASCAL 07-0365691 INIST</idno>
<idno type="RBID">Pascal:07-0365691</idno>
<idno type="wicri:Area/PascalFrancis/Corpus">000340</idno>
<idno type="wicri:Area/PascalFrancis/Curation">000446</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Key-text spotting in documentary videos using Adaboost</title>
<author><name sortKey="Lalonde, M" sort="Lalonde, M" uniqKey="Lalonde M" first="M." last="Lalonde">M. Lalonde</name>
<affiliation wicri:level="1"><inist:fA14 i1="01"><s1>R&D Department, Computer Research Institute of Montreal (CRIM), 550 Sherbrooke West, Suite 100</s1>
<s2>Montreal, QC, H3A 1B9</s2>
<s3>CAN</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
<country>Canada</country>
</affiliation>
</author>
<author><name sortKey="Gagnon, L" sort="Gagnon, L" uniqKey="Gagnon L" first="L." last="Gagnon">L. Gagnon</name>
<affiliation wicri:level="1"><inist:fA14 i1="01"><s1>R&D Department, Computer Research Institute of Montreal (CRIM), 550 Sherbrooke West, Suite 100</s1>
<s2>Montreal, QC, H3A 1B9</s2>
<s3>CAN</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</inist:fA14>
<country>Canada</country>
</affiliation>
</author>
</analytic>
<series><title level="j" type="main">Proceedings of SPIE, the International Society for Optical Engineering</title>
<idno type="ISSN">0277-786X</idno>
<imprint><date when="2006">2006</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
<seriesStmt><title level="j" type="main">Proceedings of SPIE, the International Society for Optical Engineering</title>
<idno type="ISSN">0277-786X</idno>
</seriesStmt>
</fileDesc>
<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Image processing</term>
</keywords>
<keywords scheme="Pascal" xml:lang="fr"><term>Traitement image</term>
<term>0705P</term>
<term>4230V</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">This paper presents a method for spotting key-text in videos, based on a cascade of classifiers trained with Adaboost. The video is first reduced to a set of key-frames. Each key-frame is then analyzed for its text content. Text spotting is performed by scanning the image with a variable-size window (to account for scale) within which simple features (mean/variance of grayscale values and x/y derivatives) are extracted in various sub-areas. Training builds classifiers using the most discriminant spatial combinations of features for text detection. The text-spotting module outputs a decision map of the size of the input key-frame showing regions of interest that may contain text suitable for recognition by an OCR system. Performance is measured against a dataset of 147 key-frames extracted from 22 documentary films of the National Film Board (NFB) of Canada. A detection rate of 97% is obtained with relatively few false alarms.</div>
</front>
</TEI>
<inist><standard h6="B"><pA><fA01 i1="01" i2="1"><s0>0277-786X</s0>
</fA01>
<fA05><s2>6064</s2>
</fA05>
<fA08 i1="01" i2="1" l="ENG"><s1>Key-text spotting in documentary videos using Adaboost</s1>
</fA08>
<fA09 i1="01" i2="1" l="ENG"><s1>Image processing : algorithms and systems, neural networks, and machine learning : 16-18 January 2006, San Jose, California, USA</s1>
</fA09>
<fA11 i1="01" i2="1"><s1>LALONDE (M.)</s1>
</fA11>
<fA11 i1="02" i2="1"><s1>GAGNON (L.)</s1>
</fA11>
<fA12 i1="01" i2="1"><s1>DOUGHERTY (Edward R.)</s1>
<s9>ed.</s9>
</fA12>
<fA14 i1="01"><s1>R&D Department, Computer Research Institute of Montreal (CRIM), 550 Sherbrooke West, Suite 100</s1>
<s2>Montreal, QC, H3A 1B9</s2>
<s3>CAN</s3>
<sZ>1 aut.</sZ>
<sZ>2 aut.</sZ>
</fA14>
<fA18 i1="01" i2="1"><s1>IS&T--The Society for Imaging Science and Technology</s1>
<s3>USA</s3>
<s9>org-cong.</s9>
</fA18>
<fA18 i1="02" i2="1"><s1>Society of photo-optical instrumentation engineers</s1>
<s3>USA</s3>
<s9>org-cong.</s9>
</fA18>
<fA20><s2>60641N.1-60641N.8</s2>
</fA20>
<fA21><s1>2006</s1>
</fA21>
<fA23 i1="01"><s0>ENG</s0>
</fA23>
<fA26 i1="01"><s0>0-8194-6104-0</s0>
</fA26>
<fA43 i1="01"><s1>INIST</s1>
<s2>21760</s2>
<s5>354000153558040510</s5>
</fA43>
<fA44><s0>0000</s0>
<s1>© 2007 INIST-CNRS. All rights reserved.</s1>
</fA44>
<fA45><s0>17 ref.</s0>
</fA45>
<fA47 i1="01" i2="1"><s0>07-0365691</s0>
</fA47>
<fA60><s1>P</s1>
<s2>C</s2>
</fA60>
<fA61><s0>A</s0>
</fA61>
<fA64 i1="01" i2="1"><s0>Proceedings of SPIE, the International Society for Optical Engineering</s0>
</fA64>
<fA66 i1="01"><s0>USA</s0>
</fA66>
<fC01 i1="01" l="ENG"><s0>This paper presents a method for spotting key-text in videos, based on a cascade of classifiers trained with Adaboost. The video is first reduced to a set of key-frames. Each key-frame is then analyzed for its text content. Text spotting is performed by scanning the image with a variable-size window (to account for scale) within which simple features (mean/variance of grayscale values and x/y derivatives) are extracted in various sub-areas. Training builds classifiers using the most discriminant spatial combinations of features for text detection. The text-spotting module outputs a decision map of the size of the input key-frame showing regions of interest that may contain text suitable for recognition by an OCR system. Performance is measured against a dataset of 147 key-frames extracted from 22 documentary films of the National Film Board (NFB) of Canada. A detection rate of 97% is obtained with relatively few false alarms.</s0>
</fC01>
<fC02 i1="01" i2="3"><s0>001B00G05P</s0>
</fC02>
<fC02 i1="02" i2="3"><s0>001B40B30V</s0>
</fC02>
<fC03 i1="01" i2="3" l="FRE"><s0>Traitement image</s0>
<s5>61</s5>
</fC03>
<fC03 i1="01" i2="3" l="ENG"><s0>Image processing</s0>
<s5>61</s5>
</fC03>
<fC03 i1="02" i2="3" l="FRE"><s0>0705P</s0>
<s4>INC</s4>
<s5>83</s5>
</fC03>
<fC03 i1="03" i2="3" l="FRE"><s0>4230V</s0>
<s4>INC</s4>
<s5>91</s5>
</fC03>
<fN21><s1>239</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
<pR><fA30 i1="01" i2="1" l="ENG"><s1>Image processing</s1>
<s3>USA</s3>
<s4>2006</s4>
</fA30>
</pR>
</standard>
</inist>
</record>

Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Ticri/CIDE/explor/OcrV1/Data/PascalFrancis/Curation

HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000446 | SxmlIndent | more

HfdSelect -h $EXPLOR_AREA/Data/PascalFrancis/Curation/biblio.hfd -nk 000446 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Ticri/CIDE
   |area=    OcrV1
   |flux=    PascalFrancis
   |étape=   Curation
   |type=    RBID
   |clé=     Pascal:07-0365691
   |texte=   Key-text spotting in documentary videos using Adaboost
}}

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

	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.

Serveur d'exploration sur l'OCR

Key-text spotting in documentary videos using Adaboost

Key-text spotting in documentary videos using Adaboost

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri