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Font adaptive word indexing of modern printed documents

Identifieur interne : 000409 ( PascalFrancis/Curation ); précédent : 000408; suivant : 000410

Font adaptive word indexing of modern printed documents

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

Source :

RBID : Pascal:06-0386778

Descripteurs français

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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A11 02  1    @1 MARINO (Emanuele)
A11 03  1    @1 SODA (Giovanni)
A14 01      @1 Dipartimento di Sistemi e Informatica, Università di Firenze, via di S. Marta, 3 @2 50139 Firenze @3 ITA @Z 1 aut. @Z 2 aut. @Z 3 aut.
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C01 01    ENG  @0 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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C03 04  X  SPA  @0 Presentación documento @5 04
C03 05  X  FRE  @0 Réseau web @5 05
C03 05  X  ENG  @0 World wide web @5 05
C03 05  X  SPA  @0 Red WWW @5 05
C03 06  X  FRE  @0 Reconnaissance forme @5 06
C03 06  X  ENG  @0 Pattern recognition @5 06
C03 06  X  SPA  @0 Reconocimiento patrón @5 06
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C03 07  X  ENG  @0 Character recognition @5 07
C03 07  X  SPA  @0 Reconocimiento carácter @5 07
C03 08  X  FRE  @0 Métadonnée @5 08
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C03 12  X  ENG  @0 Information retrieval @5 12
C03 12  X  SPA  @0 Búsqueda información @5 12
C03 13  3  FRE  @0 Carte autoorganisatrice @5 13
C03 13  3  ENG  @0 Self-organising feature maps @5 13
N21       @1 254
N44 01      @1 PSI
N82       @1 PSI

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Pascal:06-0386778

Le document en format XML

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