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ICDAR 2003 robust reading competitions : entries, results, and future directions

Identifieur interne : 000212 ( France/Analysis ); précédent : 000211; suivant : 000213

ICDAR 2003 robust reading competitions : entries, results, and future directions

Auteurs : Simon M. Lucas [Royaume-Uni] ; Alex Panaretos [Royaume-Uni] ; Luis Sosa [Royaume-Uni] ; Anthony Tang [Royaume-Uni] ; Shirley Wong [Royaume-Uni] ; Robert Young [Royaume-Uni] ; Kazuki Ashida [Japon] ; Hiroki Nagai [Japon] ; Masayuki Okamoto [Japon] ; Hiroaki Yamamoto [Japon] ; Hidetoshi Miyao [Japon] ; JUNMIN ZHU [République populaire de Chine] ; WUWEN OU [République populaire de Chine] ; Christian Wolf [France] ; Jean-Michel Jolion [France] ; Leon Todoran [Pays-Bas] ; Marcel Worring [Pays-Bas] ; XIAOFAN LIN [États-Unis]

Source :

RBID : Pascal:05-0421764

Descripteurs français

English descriptors

Abstract

This paper describes the robust reading competitions for ICDAR 2003. With the rapid growth in research over the last few years on recognizing text in natural scenes, there is an urgent need to establish some common benchmark datasets and gain a clear understanding of the current state of the art. We use the term 'robust reading' to refer to text images that are beyond the capabilities of current commercial OCR packages. We chose to break down the robust reading problem into three subproblems and run competitions for each stage, and also a competition for the best overall system. The subproblems we chose were text locating, character recognition and word recognition. By breaking down the problem in this way, we hoped to gain a better understanding of the state of the art in each of the subproblems. Furthermore, our methodology involved storing detailed results of applying each algorithm to each image in the datasets, allowing researchers to study in depth the strengths and weaknesses of each algorithm. The text-locating contest was the only one to have any entries. We give a brief description of each entry and present the results of this contest, showing cases where the leading entries succeed and fail. We also describe an algorithm for combining the outputs of the individual text locators and show how the combination scheme improves on any of the individual systems.


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Pascal:05-0421764

Le document en format XML

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<affiliation wicri:level="1">
<inist:fA14 i1="02">
<s1>Department of Information Engineering, Faculty of Engineering, Shinshu University</s1>
<s2>4-17-1 Wakasato Nagano 380-8553</s2>
<s3>JPN</s3>
<sZ>7 aut.</sZ>
<sZ>8 aut.</sZ>
<sZ>9 aut.</sZ>
<sZ>10 aut.</sZ>
<sZ>11 aut.</sZ>
</inist:fA14>
<country>Japon</country>
<wicri:noRegion>4-17-1 Wakasato Nagano 380-8553</wicri:noRegion>
</affiliation>
</author>
<author>
<name sortKey="Junmin Zhu" sort="Junmin Zhu" uniqKey="Junmin Zhu" last="Junmin Zhu">JUNMIN ZHU</name>
<affiliation wicri:level="1">
<inist:fA14 i1="03">
<s1>Institute of Automation, Chinese Academy of Science, PO Box 2738</s1>
<s2>Beijing 100080</s2>
<s3>CHN</s3>
<sZ>12 aut.</sZ>
<sZ>13 aut.</sZ>
</inist:fA14>
<country>République populaire de Chine</country>
<placeName>
<settlement type="city">Pékin</settlement>
</placeName>
</affiliation>
</author>
<author>
<name sortKey="Wuwen Ou" sort="Wuwen Ou" uniqKey="Wuwen Ou" last="Wuwen Ou">WUWEN OU</name>
<affiliation wicri:level="1">
<inist:fA14 i1="03">
<s1>Institute of Automation, Chinese Academy of Science, PO Box 2738</s1>
<s2>Beijing 100080</s2>
<s3>CHN</s3>
<sZ>12 aut.</sZ>
<sZ>13 aut.</sZ>
</inist:fA14>
<country>République populaire de Chine</country>
<placeName>
<settlement type="city">Pékin</settlement>
</placeName>
</affiliation>
</author>
<author>
<name sortKey="Wolf, Christian" sort="Wolf, Christian" uniqKey="Wolf C" first="Christian" last="Wolf">Christian Wolf</name>
<affiliation wicri:level="3">
<inist:fA14 i1="04">
<s1>Lyon Research Center for Images and Intelligent Information Systems (LIRIS), INSA de Lyon, Bt. J. Verne 20, rue Albert Einstein</s1>
<s2>69621 Villeurbanne</s2>
<s3>FRA</s3>
<sZ>14 aut.</sZ>
<sZ>15 aut.</sZ>
</inist:fA14>
<country>France</country>
<placeName>
<region type="region" nuts="2">Auvergne-Rhône-Alpes</region>
<region type="old region" nuts="2">Rhône-Alpes</region>
<settlement type="city">Villeurbanne</settlement>
</placeName>
</affiliation>
</author>
<author>
<name sortKey="Jolion, Jean Michel" sort="Jolion, Jean Michel" uniqKey="Jolion J" first="Jean-Michel" last="Jolion">Jean-Michel Jolion</name>
<affiliation wicri:level="3">
<inist:fA14 i1="04">
<s1>Lyon Research Center for Images and Intelligent Information Systems (LIRIS), INSA de Lyon, Bt. J. Verne 20, rue Albert Einstein</s1>
<s2>69621 Villeurbanne</s2>
<s3>FRA</s3>
<sZ>14 aut.</sZ>
<sZ>15 aut.</sZ>
</inist:fA14>
<country>France</country>
<placeName>
<region type="region" nuts="2">Auvergne-Rhône-Alpes</region>
<region type="old region" nuts="2">Rhône-Alpes</region>
<settlement type="city">Villeurbanne</settlement>
</placeName>
</affiliation>
</author>
<author>
<name sortKey="Todoran, Leon" sort="Todoran, Leon" uniqKey="Todoran L" first="Leon" last="Todoran">Leon Todoran</name>
<affiliation wicri:level="1">
<inist:fA14 i1="05">
<s1>Informatics Institute, University of Amsterdam, Kruislaan 403</s1>
<s2>1098 SJ Amsterdam</s2>
<s3>NLD</s3>
<sZ>16 aut.</sZ>
<sZ>17 aut.</sZ>
</inist:fA14>
<country>Pays-Bas</country>
<wicri:noRegion>1098 SJ Amsterdam</wicri:noRegion>
</affiliation>
</author>
<author>
<name sortKey="Worring, Marcel" sort="Worring, Marcel" uniqKey="Worring M" first="Marcel" last="Worring">Marcel Worring</name>
<affiliation wicri:level="1">
<inist:fA14 i1="05">
<s1>Informatics Institute, University of Amsterdam, Kruislaan 403</s1>
<s2>1098 SJ Amsterdam</s2>
<s3>NLD</s3>
<sZ>16 aut.</sZ>
<sZ>17 aut.</sZ>
</inist:fA14>
<country>Pays-Bas</country>
<wicri:noRegion>1098 SJ Amsterdam</wicri:noRegion>
</affiliation>
</author>
<author>
<name sortKey="Xiaofan Lin" sort="Xiaofan Lin" uniqKey="Xiaofan Lin" last="Xiaofan Lin">XIAOFAN LIN</name>
<affiliation wicri:level="2">
<inist:fA14 i1="06">
<s1>Hewlett-Packard Laboratories, 1501 Page Mill Road, MS 1203</s1>
<s2>Palo Alto, CA 94304</s2>
<s3>USA</s3>
<sZ>18 aut.</sZ>
</inist:fA14>
<country>États-Unis</country>
<placeName>
<region type="state">Californie</region>
</placeName>
</affiliation>
</author>
</analytic>
<series>
<title level="j" type="main">International journal on document analysis and recognition : (Print)</title>
<title level="j" type="abbreviated">Int. j. doc. anal. recognit. : (Print)</title>
<idno type="ISSN">1433-2833</idno>
<imprint>
<date when="2005">2005</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
<seriesStmt>
<title level="j" type="main">International journal on document analysis and recognition : (Print)</title>
<title level="j" type="abbreviated">Int. j. doc. anal. recognit. : (Print)</title>
<idno type="ISSN">1433-2833</idno>
</seriesStmt>
</fileDesc>
<profileDesc>
<textClass>
<keywords scheme="KwdEn" xml:lang="en">
<term>Character recognition</term>
<term>Fingerprint</term>
<term>Optical character recognition</term>
<term>Pattern recognition</term>
</keywords>
<keywords scheme="Pascal" xml:lang="fr">
<term>Reconnaissance forme</term>
<term>Reconnaissance optique caractère</term>
<term>Reconnaissance caractère</term>
<term>Empreinte digitale</term>
<term>Reconnaissance mot</term>
<term>Localisation texte</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front>
<div type="abstract" xml:lang="en">This paper describes the robust reading competitions for ICDAR 2003. With the rapid growth in research over the last few years on recognizing text in natural scenes, there is an urgent need to establish some common benchmark datasets and gain a clear understanding of the current state of the art. We use the term 'robust reading' to refer to text images that are beyond the capabilities of current commercial OCR packages. We chose to break down the robust reading problem into three subproblems and run competitions for each stage, and also a competition for the best overall system. The subproblems we chose were text locating, character recognition and word recognition. By breaking down the problem in this way, we hoped to gain a better understanding of the state of the art in each of the subproblems. Furthermore, our methodology involved storing detailed results of applying each algorithm to each image in the datasets, allowing researchers to study in depth the strengths and weaknesses of each algorithm. The text-locating contest was the only one to have any entries. We give a brief description of each entry and present the results of this contest, showing cases where the leading entries succeed and fail. We also describe an algorithm for combining the outputs of the individual text locators and show how the combination scheme improves on any of the individual systems.</div>
</front>
</TEI>
<affiliations>
<list>
<country>
<li>France</li>
<li>Japon</li>
<li>Pays-Bas</li>
<li>Royaume-Uni</li>
<li>République populaire de Chine</li>
<li>États-Unis</li>
</country>
<region>
<li>Auvergne-Rhône-Alpes</li>
<li>Californie</li>
<li>Rhône-Alpes</li>
</region>
<settlement>
<li>Pékin</li>
<li>Villeurbanne</li>
</settlement>
</list>
<tree>
<country name="Royaume-Uni">
<noRegion>
<name sortKey="Lucas, Simon M" sort="Lucas, Simon M" uniqKey="Lucas S" first="Simon M." last="Lucas">Simon M. Lucas</name>
</noRegion>
<name sortKey="Panaretos, Alex" sort="Panaretos, Alex" uniqKey="Panaretos A" first="Alex" last="Panaretos">Alex Panaretos</name>
<name sortKey="Sosa, Luis" sort="Sosa, Luis" uniqKey="Sosa L" first="Luis" last="Sosa">Luis Sosa</name>
<name sortKey="Tang, Anthony" sort="Tang, Anthony" uniqKey="Tang A" first="Anthony" last="Tang">Anthony Tang</name>
<name sortKey="Wong, Shirley" sort="Wong, Shirley" uniqKey="Wong S" first="Shirley" last="Wong">Shirley Wong</name>
<name sortKey="Young, Robert" sort="Young, Robert" uniqKey="Young R" first="Robert" last="Young">Robert Young</name>
</country>
<country name="Japon">
<noRegion>
<name sortKey="Ashida, Kazuki" sort="Ashida, Kazuki" uniqKey="Ashida K" first="Kazuki" last="Ashida">Kazuki Ashida</name>
</noRegion>
<name sortKey="Miyao, Hidetoshi" sort="Miyao, Hidetoshi" uniqKey="Miyao H" first="Hidetoshi" last="Miyao">Hidetoshi Miyao</name>
<name sortKey="Nagai, Hiroki" sort="Nagai, Hiroki" uniqKey="Nagai H" first="Hiroki" last="Nagai">Hiroki Nagai</name>
<name sortKey="Okamoto, Masayuki" sort="Okamoto, Masayuki" uniqKey="Okamoto M" first="Masayuki" last="Okamoto">Masayuki Okamoto</name>
<name sortKey="Yamamoto, Hiroaki" sort="Yamamoto, Hiroaki" uniqKey="Yamamoto H" first="Hiroaki" last="Yamamoto">Hiroaki Yamamoto</name>
</country>
<country name="République populaire de Chine">
<noRegion>
<name sortKey="Junmin Zhu" sort="Junmin Zhu" uniqKey="Junmin Zhu" last="Junmin Zhu">JUNMIN ZHU</name>
</noRegion>
<name sortKey="Wuwen Ou" sort="Wuwen Ou" uniqKey="Wuwen Ou" last="Wuwen Ou">WUWEN OU</name>
</country>
<country name="France">
<region name="Auvergne-Rhône-Alpes">
<name sortKey="Wolf, Christian" sort="Wolf, Christian" uniqKey="Wolf C" first="Christian" last="Wolf">Christian Wolf</name>
</region>
<name sortKey="Jolion, Jean Michel" sort="Jolion, Jean Michel" uniqKey="Jolion J" first="Jean-Michel" last="Jolion">Jean-Michel Jolion</name>
</country>
<country name="Pays-Bas">
<noRegion>
<name sortKey="Todoran, Leon" sort="Todoran, Leon" uniqKey="Todoran L" first="Leon" last="Todoran">Leon Todoran</name>
</noRegion>
<name sortKey="Worring, Marcel" sort="Worring, Marcel" uniqKey="Worring M" first="Marcel" last="Worring">Marcel Worring</name>
</country>
<country name="États-Unis">
<region name="Californie">
<name sortKey="Xiaofan Lin" sort="Xiaofan Lin" uniqKey="Xiaofan Lin" last="Xiaofan Lin">XIAOFAN LIN</name>
</region>
</country>
</tree>
</affiliations>
</record>

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