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 :
-
SPIE proceedings series [ 1017-2653 ] ; 2005.
RBID : Pascal:05-0361255
Descripteurs français
- Pascal (Inist)
- Reconnaissance caractère,
Implémentation,
Traitement document,
Reconnaissance optique caractère,
Analyse documentaire,
Image multiple,
Algorithme,
Classification automatique,
Réseau neuronal,
Evaluation performance,
Reconnaissance forme,
Classification signal,
Extraction caractéristique,
Traitement signal.
English descriptors
- KwdEn :
- Algorithm,
Automatic classification,
Character recognition,
Document analysis,
Document processing,
Feature extraction,
Implementation,
Multiple image,
Neural network,
Optical character recognition,
Pattern recognition,
Performance evaluation,
Signal classification,
Signal processing.
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.
pA |
A01 | 01 | 1 | | @0 1017-2653 |
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A05 | | | | @2 5676 |
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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 |
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A09 | 01 | 1 | ENG | @1 Document recognition and retrieval XII : San Jose CA, 19-20 January 2005 |
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A11 | 01 | 1 | | @1 HOPKINS (Jared) |
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A11 | 02 | 1 | | @1 ANDERSEN (Tim) |
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A12 | 01 | 1 | | @1 SMITH (Elisa H. Barney) @9 ed. |
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A12 | 02 | 1 | | @1 TAGHVA (Kazem) @9 ed. |
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A14 | 01 | | | @1 Computer Science Department, Boise State University @2 Boise, Idaho 83725 @3 USA @Z 1 aut. @Z 2 aut. |
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A18 | 01 | 1 | | @1 International Society for Optical Engineering @2 Bellingham WA @3 USA @9 org-cong. |
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A20 | | | | @1 111-118 |
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A21 | | | | @1 2005 |
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A23 | 01 | | | @0 ENG |
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A43 | 01 | | | @1 INIST @2 21760 @5 354000124499720130 |
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A44 | | | | @0 0000 @1 © 2005 INIST-CNRS. All rights reserved. |
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A45 | | | | @0 11 ref. |
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A47 | 01 | 1 | | @0 05-0361255 |
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A61 | | | | @0 A |
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A64 | 01 | 1 | | @0 SPIE proceedings series |
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A66 | 01 | | | @0 USA |
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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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C02 | 02 | X | | @0 001D02C06 |
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C02 | 03 | X | | @0 001D04A04A1 |
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C03 | 01 | X | FRE | @0 Reconnaissance caractère @5 01 |
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C03 | 01 | X | ENG | @0 Character recognition @5 01 |
---|
C03 | 01 | X | SPA | @0 Reconocimiento carácter @5 01 |
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C03 | 02 | X | FRE | @0 Implémentation @5 02 |
---|
C03 | 02 | X | ENG | @0 Implementation @5 02 |
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C03 | 02 | X | SPA | @0 Implementación @5 02 |
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C03 | 03 | X | FRE | @0 Traitement document @5 03 |
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C03 | 03 | X | ENG | @0 Document processing @5 03 |
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C03 | 03 | X | SPA | @0 Tratamiento documento @5 03 |
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C03 | 04 | X | FRE | @0 Reconnaissance optique caractère @5 04 |
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C03 | 04 | X | ENG | @0 Optical character recognition @5 04 |
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C03 | 04 | X | SPA | @0 Reconocimento óptico de caracteres @5 04 |
---|
C03 | 05 | X | FRE | @0 Analyse documentaire @5 05 |
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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 |
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C03 | 07 | X | FRE | @0 Algorithme @5 07 |
---|
C03 | 07 | X | ENG | @0 Algorithm @5 07 |
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C03 | 07 | X | SPA | @0 Algoritmo @5 07 |
---|
C03 | 08 | X | FRE | @0 Classification automatique @5 08 |
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C03 | 08 | X | ENG | @0 Automatic classification @5 08 |
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C03 | 08 | X | SPA | @0 Clasificación automática @5 08 |
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C03 | 09 | X | FRE | @0 Réseau neuronal @5 09 |
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C03 | 09 | X | ENG | @0 Neural network @5 09 |
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C03 | 09 | X | SPA | @0 Red neuronal @5 09 |
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C03 | 10 | X | FRE | @0 Evaluation performance @5 10 |
---|
C03 | 10 | X | ENG | @0 Performance evaluation @5 10 |
---|
C03 | 10 | X | SPA | @0 Evaluación prestación @5 10 |
---|
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 |
---|
C03 | 12 | 3 | ENG | @0 Signal classification @5 32 |
---|
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 |
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C03 | 14 | X | ENG | @0 Signal processing @5 34 |
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C03 | 14 | X | SPA | @0 Procesamiento señal @5 34 |
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N21 | | | | @1 248 |
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N44 | 01 | | | @1 OTO |
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N82 | | | | @1 OTO |
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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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