OcrV1, Main, Exploration, bibRecord, 001351

A Robust Free Size OCR for Omni-Font Persian/Arabic Printed Document Using Combined MLP/SVM

Identifieur interne : 001351 ( Main/Exploration ); précédent : 001350; suivant : 001352

A Robust Free Size OCR for Omni-Font Persian/Arabic Printed Document Using Combined MLP/SVM

Auteurs : Hamed Pirsiavash [Iran, États-Unis] ; Ramin Mehran [Iran] ; Farbod Razzazi [Iran]

Source :

Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.

RBID : ISTEX:BD87C05A1B17AB85ECFAF19962583CA3FC852CB9

Descripteurs français

Pascal (Inist)
- ., Arabe, Base donnée, Caractère imprimé, Document imprimé, Intelligence artificielle, Intérêt commercial, Précision élevée, Reconnaissance caractère, Reconnaissance forme, Reconnaissance optique caractère, Réseau multicouche, Réseau neuronal, Segmentation, Traitement image.
Wicri :
- topic : Base de données, Intelligence artificielle.

English descriptors

KwdEn :
- Arabic, Artificial intelligence, Character recognition, Commercial use, Database, High precision, Image processing, Multilayer network, Neural network, Optical character recognition, Pattern recognition, Printed character, Printed document, Segmentation.

Abstract

Abstract: Optical character recognition of cursive scripts present a number of challenging problems in both segmentation and recognition processes and this attracts many researches in the field of machine learning. This paper presents a novel approach based on a combination of MLP and SVM to design a trainable OCR for Persian/Arabic cursive documents. The implementation results on a comprehensive database show a high degree of accuracy which meets the requirements of commercial use.

Url:

https://api.istex.fr/document/BD87C05A1B17AB85ECFAF19962583CA3FC852CB9/fulltext/pdf

DOI: 10.1007/11578079_63

Affiliations:

Iran, États-Unis

Le document en format XML

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A Robust Free Size OCR for Omni-Font Persian/Arabic Printed Document Using Combined MLP/SVM

A Robust Free Size OCR for Omni-Font Persian/Arabic Printed Document Using Combined MLP/SVM

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

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