OcrV1, Main, Curation, bibRecord, 001167

Font adaptive word indexing of modern printed documents

Identifieur interne : 001167 ( Main/Curation ); précédent : 001166; suivant : 001168

Font adaptive word indexing of modern printed documents

Auteurs : Simone Marinai [Italie] ; Emanuele Marino [Italie] ; Giovanni Soda [Italie]

Source :

IEEE transactions on pattern analysis and machine intelligence [ 0162-8828 ] ; 2006.

RBID : Pascal:06-0386778

Descripteurs français

Pascal (Inist)
- Reconnaissance image, Analyse forme, Traitement image, Présentation document, Réseau web, Reconnaissance forme, Reconnaissance caractère, Métadonnée, Navigation information, Bibliothèque électronique, Interrogation base donnée, Recherche information, Carte autoorganisatrice.

English descriptors

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.

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Le document en format XML

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</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Abstracting and Indexing as Topic</term>
<term>Automatic Data Processing</term>
<term>Documentation</term>
<term>Image Enhancement</term>
<term>Image Interpretation, Computer-Assisted</term>
<term>Information Storage and Retrieval</term>
<term>Pattern Recognition, Automated</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Artificial Intelligence</term>
<term>Computer Graphics</term>
<term>Libraries, Digital</term>
<term>Natural Language Processing</term>
<term>Publishing</term>
<term>Reproducibility of Results</term>
<term>Semantics</term>
<term>Sensitivity and Specificity</term>
<term>Signal Processing, Computer-Assisted</term>
<term>Subtraction Technique</term>
<term>User-Computer Interface</term>
<term>Vocabulary, Controlled</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">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.</div>
</front>
</TEI>
</PubMed>
</double>
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   |texte=   Font adaptive word indexing of modern printed documents
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Font adaptive word indexing of modern printed documents

Font adaptive word indexing of modern printed documents

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