Font adaptive word indexing of modern printed documents.
Identifieur interne : 000063 ( PubMed/Curation ); précédent : 000062; suivant : 000064Font adaptive word indexing of modern printed documents.
Auteurs : Simone Marinai [Italie] ; Emanuele Marino ; Giovanni SodaSource :
- IEEE transactions on pattern analysis and machine intelligence [ 0162-8828 ] ; 2006.
English descriptors
- KwdEn :
- Abstracting and Indexing as Topic (methods), Algorithms, Artificial Intelligence, Automatic Data Processing (methods), Computer Graphics, Documentation (methods), Image Enhancement (methods), Image Interpretation, Computer-Assisted (methods), Information Storage and Retrieval (methods), Libraries, Digital, Natural Language Processing, Pattern Recognition, Automated (methods), Publishing, Reproducibility of Results, Semantics, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Subtraction Technique, User-Computer Interface, Vocabulary, Controlled.
- MESH :
- methods : Abstracting and Indexing as Topic, Automatic Data Processing, Documentation, Image Enhancement, Image Interpretation, Computer-Assisted, Information Storage and Retrieval, Pattern Recognition, Automated.
- Algorithms, Artificial Intelligence, Computer Graphics, Libraries, Digital, Natural Language Processing, Publishing, Reproducibility of Results, Semantics, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Subtraction Technique, User-Computer Interface, Vocabulary, Controlled.
Abstract
We propose an approach for the word-level indexing of modern printed documents which are difficult to recognize using current OCR engines. By means of word-level indexing, it is possible to retrieve the position of words in a document, enabling queries involving proximity of terms. Web search engines implement this kind of indexing, allowing users to retrieve Web pages on the basis of their textual content. Nowadays, digital libraries hold collections of digitized documents that can be retrieved either by browsing the document images or relying on appropriate metadata assembled by domain experts. Word indexing tools would therefore increase the access to these collections. The proposed system is designed to index homogeneous document collections by automatically adapting to different languages and font styles without relying on OCR engines for character recognition. The approach is based on three main ideas: the use of Self Organizing Maps (SOM) to perform unsupervised character clustering, the definition of one suitable vector-based word representation whose size depends on the word aspect-ratio, and the run-time alignment of the query word with indexed words to deal with broken and touching characters. The most appropriate applications are for processing modern printed documents (17th to 19th centuries) where current OCR engines are less accurate. Our experimental analysis addresses six data sets containing documents ranging from books of the 17th century to contemporary journals.
DOI: 10.1109/TPAMI.2006.162
PubMed: 16886856
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Marta, 3, 50139 Firenze</wicri:regionArea></affiliation></author><author><name sortKey="Marino, Emanuele" sort="Marino, Emanuele" uniqKey="Marino E" first="Emanuele" last="Marino">Emanuele Marino</name></author><author><name sortKey="Soda, Giovanni" sort="Soda, Giovanni" uniqKey="Soda G" first="Giovanni" last="Soda">Giovanni Soda</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2006">2006</date><idno type="RBID">pubmed:16886856</idno><idno type="pmid">16886856</idno><idno type="doi">10.1109/TPAMI.2006.162</idno><idno type="wicri:Area/PubMed/Corpus">000063</idno><idno type="wicri:Area/PubMed/Curation">000063</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Font adaptive word indexing of modern printed documents.</title><author><name sortKey="Marinai, Simone" sort="Marinai, Simone" uniqKey="Marinai S" first="Simone" last="Marinai">Simone Marinai</name><affiliation wicri:level="1"><nlm:affiliation>Dipartimento di Sistenmi e Informatica, Università di Firenze, via di S. 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By means of word-level indexing, it is possible to retrieve the position of words in a document, enabling queries involving proximity of terms. Web search engines implement this kind of indexing, allowing users to retrieve Web pages on the basis of their textual content. Nowadays, digital libraries hold collections of digitized documents that can be retrieved either by browsing the document images or relying on appropriate metadata assembled by domain experts. Word indexing tools would therefore increase the access to these collections. The proposed system is designed to index homogeneous document collections by automatically adapting to different languages and font styles without relying on OCR engines for character recognition. The approach is based on three main ideas: the use of Self Organizing Maps (SOM) to perform unsupervised character clustering, the definition of one suitable vector-based word representation whose size depends on the word aspect-ratio, and the run-time alignment of the query word with indexed words to deal with broken and touching characters. The most appropriate applications are for processing modern printed documents (17th to 19th centuries) where current OCR engines are less accurate. Our experimental analysis addresses six data sets containing documents ranging from books of the 17th century to contemporary journals.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">16886856</PMID><DateCreated><Year>2006</Year><Month>08</Month><Day>04</Day></DateCreated><DateCompleted><Year>2006</Year><Month>09</Month><Day>05</Day></DateCompleted><DateRevised><Year>2007</Year><Month>11</Month><Day>15</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">0162-8828</ISSN><JournalIssue CitedMedium="Print"><Volume>28</Volume><Issue>8</Issue><PubDate><Year>2006</Year><Month>Aug</Month></PubDate></JournalIssue><Title>IEEE transactions on pattern analysis and machine intelligence</Title><ISOAbbreviation>IEEE Trans Pattern Anal Mach Intell</ISOAbbreviation></Journal><ArticleTitle>Font adaptive word indexing of modern printed documents.</ArticleTitle><Pagination><MedlinePgn>1187-99</MedlinePgn></Pagination><Abstract><AbstractText>We propose an approach for the word-level indexing of modern printed documents which are difficult to recognize using current OCR engines. By means of word-level indexing, it is possible to retrieve the position of words in a document, enabling queries involving proximity of terms. Web search engines implement this kind of indexing, allowing users to retrieve Web pages on the basis of their textual content. Nowadays, digital libraries hold collections of digitized documents that can be retrieved either by browsing the document images or relying on appropriate metadata assembled by domain experts. Word indexing tools would therefore increase the access to these collections. The proposed system is designed to index homogeneous document collections by automatically adapting to different languages and font styles without relying on OCR engines for character recognition. The approach is based on three main ideas: the use of Self Organizing Maps (SOM) to perform unsupervised character clustering, the definition of one suitable vector-based word representation whose size depends on the word aspect-ratio, and the run-time alignment of the query word with indexed words to deal with broken and touching characters. The most appropriate applications are for processing modern printed documents (17th to 19th centuries) where current OCR engines are less accurate. Our experimental analysis addresses six data sets containing documents ranging from books of the 17th century to contemporary journals.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Marinai</LastName><ForeName>Simone</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Dipartimento di Sistenmi e Informatica, Università di Firenze, via di S. Marta, 3, 50139 Firenze, Italy. marinai@dsi.unifi.it</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Marino</LastName><ForeName>Emanuele</ForeName><Initials>E</Initials></Author><Author ValidYN="Y"><LastName>Soda</LastName><ForeName>Giovanni</ForeName><Initials>G</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D023362">Evaluation Studies</PublicationType><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>IEEE Trans Pattern Anal Mach Intell</MedlineTA><NlmUniqueID>9885960</NlmUniqueID><ISSNLinking>0098-5589</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000043">Abstracting and Indexing as Topic</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000465">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D001185">Artificial Intelligence</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D001330">Automatic Data Processing</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D003196">Computer Graphics</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D004282">Documentation</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D007089">Image Enhancement</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D007090">Image Interpretation, Computer-Assisted</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D016247">Information Storage and Retrieval</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D046609">Libraries, Digital</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D009323">Natural Language Processing</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D010363">Pattern Recognition, Automated</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D011643">Publishing</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D015203">Reproducibility of Results</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D012660">Semantics</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D012680">Sensitivity and Specificity</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D012815">Signal Processing, Computer-Assisted</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D013382">Subtraction Technique</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D014584">User-Computer Interface</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D018875">Vocabulary, Controlled</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="pubmed"><Year>2006</Year><Month>8</Month><Day>5</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2006</Year><Month>9</Month><Day>6</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2006</Year><Month>8</Month><Day>5</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">16886856</ArticleId><ArticleId IdType="doi">10.1109/TPAMI.2006.162</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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