OcrV1, PascalFrancis, Checkpoint, bibRecord, 000A10

High accuracy optical character recognition using neural networks with centroid dithering

Identifieur interne : 000A10 ( PascalFrancis/Checkpoint ); précédent : 000A09; suivant : 000A11

High accuracy optical character recognition using neural networks with centroid dithering

Auteurs : H. I. Avi-Itzhak [États-Unis] ; T. A. Diep [États-Unis] ; H. Garland

Source :

IEEE transactions on pattern analysis and machine intelligence [ 0162-8828 ] ; 1995.

RBID : Pascal:95-0146490

Descripteurs français

Pascal (Inist)
- Reconnaissance caractère, Reconnaissance forme, Image bruitée, Réseau neuronal, Base donnée, Jeu caractère, Optical character recognition, Multi font preprocessing, Multi size preprocessing.
Wicri :
- topic : Base de données.

English descriptors

KwdEn :
- Character recognition, Character set, Database, Neural network, Noisy image, Pattern recognition.

Abstract

Optical character recognition (OCR) refers to a process whereby printed documents are transformed into ASCII Files for the purpose of compact storage, editing, fast retrieval, and other File manipulations through the use of a computer. The recognition stage of an OCR process is made difficult by added noise, image distortion, and the various character typefaces, sizes, and fonts that a document may have. In this study a neural network approach is introduced to perform high accuracy recognition on multi-size and multi-font characters; a novel centroid-dithering training process with a low noise-sensitivity normalization procedure is used to achieve high accuracy results. The study consists of two parts. The first part focuses on single size and single font characters, and a two-layered neural network is trained to recognize the full set of 94 ASCII character images in 12-pt Courier font. The second part trades accuracy for additional font and size capability, and a larger two-layered neural network is trained to recognize the full set of 94 ASCII character images for all point sizes from 8 to 32 and for 12 commonly used fonts. The performance of these two networks is evaluated based on a database of more than one million character images from the testing data set

Affiliations:

États-Unis

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Pascal:95-0146490

Le document en format XML

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High accuracy optical character recognition using neural networks with centroid dithering

High accuracy optical character recognition using neural networks with centroid dithering

Source :

Descripteurs français

English descriptors

Abstract

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

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri