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A fourier descriptor based character recognition engine implemented under the gamera open-source document processing framework

Identifieur interne : 000331 ( PascalFrancis/Curation ); précédent : 000330; suivant : 000332

A fourier descriptor based character recognition engine implemented under the gamera open-source document processing framework

Auteurs : Jared Hopkins [États-Unis] ; Tim Andersen [États-Unis]

Source :

RBID : Pascal:05-0361255

Descripteurs français

English descriptors

Abstract

This paper discusses the implementation of an engine for performing optical character recognition of bi-tonal images using the Gamera framework, an existing open-source framework for building document analysis applications. The OCR engine uses features that are based on the Fourier descriptor to distinguish characters, and is designed to be able to handle character images that contain multiple boundaries. The algorithm works by assigning to each character image a signature that encodes the boundary types that are present in the image as well as the positional relationships that exist between them. Under this approach, only images having the same signature are comparable. Effectively, a meta-classifier is used which first computes the signature of an input image and then dispatches the image to an underlying neural network based classifier which is trained to distinguish between images having that signature. The performance of the OCR engine is evaluated on a set of sample images taken from the newspaper domain, and compares well with other OCR engines. The source code for this engine and all supporting modules is currently available upon request, and will eventually be made available through an open-source project on the sourceforge website.
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A08 01  1  ENG  @1 A fourier descriptor based character recognition engine implemented under the gamera open-source document processing framework
A09 01  1  ENG  @1 Document recognition and retrieval XII : San Jose CA, 19-20 January 2005
A11 01  1    @1 HOPKINS (Jared)
A11 02  1    @1 ANDERSEN (Tim)
A12 01  1    @1 SMITH (Elisa H. Barney) @9 ed.
A12 02  1    @1 TAGHVA (Kazem) @9 ed.
A14 01      @1 Computer Science Department, Boise State University @2 Boise, Idaho 83725 @3 USA @Z 1 aut. @Z 2 aut.
A18 01  1    @1 International Society for Optical Engineering @2 Bellingham WA @3 USA @9 org-cong.
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C01 01    ENG  @0 This paper discusses the implementation of an engine for performing optical character recognition of bi-tonal images using the Gamera framework, an existing open-source framework for building document analysis applications. The OCR engine uses features that are based on the Fourier descriptor to distinguish characters, and is designed to be able to handle character images that contain multiple boundaries. The algorithm works by assigning to each character image a signature that encodes the boundary types that are present in the image as well as the positional relationships that exist between them. Under this approach, only images having the same signature are comparable. Effectively, a meta-classifier is used which first computes the signature of an input image and then dispatches the image to an underlying neural network based classifier which is trained to distinguish between images having that signature. The performance of the OCR engine is evaluated on a set of sample images taken from the newspaper domain, and compares well with other OCR engines. The source code for this engine and all supporting modules is currently available upon request, and will eventually be made available through an open-source project on the sourceforge website.
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C03 01  X  SPA  @0 Reconocimiento carácter @5 01
C03 02  X  FRE  @0 Implémentation @5 02
C03 02  X  ENG  @0 Implementation @5 02
C03 02  X  SPA  @0 Implementación @5 02
C03 03  X  FRE  @0 Traitement document @5 03
C03 03  X  ENG  @0 Document processing @5 03
C03 03  X  SPA  @0 Tratamiento documento @5 03
C03 04  X  FRE  @0 Reconnaissance optique caractère @5 04
C03 04  X  ENG  @0 Optical character recognition @5 04
C03 04  X  SPA  @0 Reconocimento óptico de caracteres @5 04
C03 05  X  FRE  @0 Analyse documentaire @5 05
C03 05  X  ENG  @0 Document analysis @5 05
C03 05  X  SPA  @0 Análisis documental @5 05
C03 06  X  FRE  @0 Image multiple @5 06
C03 06  X  ENG  @0 Multiple image @5 06
C03 06  X  SPA  @0 Imagen múltiple @5 06
C03 07  X  FRE  @0 Algorithme @5 07
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C03 08  X  FRE  @0 Classification automatique @5 08
C03 08  X  ENG  @0 Automatic classification @5 08
C03 08  X  SPA  @0 Clasificación automática @5 08
C03 09  X  FRE  @0 Réseau neuronal @5 09
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C03 11  X  FRE  @0 Reconnaissance forme @5 31
C03 11  X  ENG  @0 Pattern recognition @5 31
C03 11  X  SPA  @0 Reconocimiento patrón @5 31
C03 12  3  FRE  @0 Classification signal @5 32
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C03 13  3  FRE  @0 Extraction caractéristique @5 33
C03 13  3  ENG  @0 Feature extraction @5 33
C03 14  X  FRE  @0 Traitement signal @5 34
C03 14  X  ENG  @0 Signal processing @5 34
C03 14  X  SPA  @0 Procesamiento señal @5 34
N21       @1 248
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pR  
A30 01  1  ENG  @1 Document recognition and retrieval. Conference @2 12 @3 San Jose CA USA @4 2005-01-19

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Le document en format XML

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