OcrV1, Main, Exploration, bibRecord, 000D19

Efficient Implementation of Local Adaptive Thresholding Techniques Using Integral Images

Identifieur interne : 000D19 ( Main/Exploration ); précédent : 000D18; suivant : 000D20

Efficient Implementation of Local Adaptive Thresholding Techniques Using Integral Images

Auteurs : Faisal Shafait [Allemagne] ; Daniel Keysers [Allemagne] ; Thomas M. Breuel [Allemagne]

Source :

Proceedings electronic imaging science and technology

RBID : Pascal:08-0400010

Descripteurs français

Pascal (Inist)
- Algorithme adaptatif, Implémentation, Méthode adaptative, Détection seuil, Analyse documentaire, Reconnaissance optique caractère, Reconnaissance forme, 0130C, 4230S.

English descriptors

KwdEn :
- Adaptive algorithm, Adaptive method, Document analysis, Implementation, Optical character recognition, Pattern recognition, Threshold detection.

Abstract

Adaptive binarization is an important first step in many document analysis and OCR processes. This paper describes a fast adaptive binarization algorithm that yields the same quality of binarization as the Sauvola method,¹ but runs in time close to that of global thresholding methods (like Otsu's method²), independent of the window size. The algorithm combines the statistical constraints of Sauvola's method with integral images.³ Testing on the UW-1 dataset demonstrates a 20-fold speedup compared to the original Sauvola algorithm.

Affiliations:

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

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to stream PascalFrancis, to step Curation: 000517
to stream PascalFrancis, to step Checkpoint: 000241
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to stream Main, to step Curation: 000D19

Le document en format XML

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<front><div type="abstract" xml:lang="en">Adaptive binarization is an important first step in many document analysis and OCR processes. This paper describes a fast adaptive binarization algorithm that yields the same quality of binarization as the Sauvola method,<sup>1</sup>
 but runs in time close to that of global thresholding methods (like Otsu's method<sup>2</sup>
), independent of the window size. The algorithm combines the statistical constraints of Sauvola's method with integral images.<sup>3</sup>
 Testing on the UW-1 dataset demonstrates a 20-fold speedup compared to the original Sauvola algorithm.</div>
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Efficient Implementation of Local Adaptive Thresholding Techniques Using Integral Images

Efficient Implementation of Local Adaptive Thresholding Techniques Using Integral Images

Source :

Descripteurs français

English descriptors

Abstract

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

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri