OcrV1, Main, Exploration, bibRecord, 001E55

A Document skew detection method using the hough transform

Identifieur interne : 001E55 ( Main/Exploration ); précédent : 001E54; suivant : 001E56

A Document skew detection method using the hough transform

Auteurs : A. Amin [Australie] ; S. Fischer [Australie]

Source :

Pattern analysis and applications [ 1433-7541 ] ; 2000.

RBID : Pascal:00-0489848

Descripteurs français

Pascal (Inist)
- Scanneur, Reconnaissance caractère, Système optique, Reconnaissance forme, Traitement image, Interprétation image, Traitement document, Evaluation performance, Segmentation, Lecteur optique, Transformation Hough, Automatisation, Documentation, Texte, Méthode moindre carré, Projection, Profil, Reconnaissance optique caractère, Composante connexe.
Wicri :
- topic : Automatisation, Documentation.

English descriptors

KwdEn :
- Automation, Character recognition, Document processing, Documentation, Hough transformation, Image interpretation, Image processing, Least squares method, Optical character recognition, Optical reader, Optical system, Pattern recognition, Performance evaluation, Profile, Projection, Scanner, Segmentation, Text.

Abstract

Document image processing has become an increasingly important technology in the automation of office documentation tasks. Automatic document scanners such as text readers and OCR (Optical Character Recognition) systems are an essential component of systems capable of those tasks. One of the problems in this field is that the document to be read is not always placed correctly on a flatbed scanner. This means that the document may be skewed on the scanner bed, resulting in a skewed image. This skew has a detrimental effect on document analysis, document understanding, and character segmentation and recognition. Consequently, detecting the skew of a document image and correcting it are important issues in realising a practical document reader. In this paper we describe a new algorithm for skew detection. We then compare the performance and results of this skew detection algorithm to other published methods from O'Gorman, Hinds, Le, Baird, Postl and Akiyama. Finally, we discuss the theory of skew detection and the different approaches taken to solve the problem of skew in documents. The skew correction algorithm we propose has been shown to he extremely fast, with run times averaging under 0.25 CPU seconds to calculate the angle on a DEC 5000/20 workstation.

Affiliations:

Australie

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

to stream PascalFrancis, to step Corpus: 000763
to stream PascalFrancis, to step Curation: 000031
to stream PascalFrancis, to step Checkpoint: 000752
to stream Main, to step Merge: 001F64
to stream Main, to step Curation: 001E55

Le document en format XML

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<term>Interprétation image</term>
<term>Traitement document</term>
<term>Evaluation performance</term>
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<front><div type="abstract" xml:lang="en">Document image processing has become an increasingly important technology in the automation of office documentation tasks. Automatic document scanners such as text readers and OCR (Optical Character Recognition) systems are an essential component of systems capable of those tasks. One of the problems in this field is that the document to be read is not always placed correctly on a flatbed scanner. This means that the document may be skewed on the scanner bed, resulting in a skewed image. This skew has a detrimental effect on document analysis, document understanding, and character segmentation and recognition. Consequently, detecting the skew of a document image and correcting it are important issues in realising a practical document reader. In this paper we describe a new algorithm for skew detection. We then compare the performance and results of this skew detection algorithm to other published methods from O'Gorman, Hinds, Le, Baird, Postl and Akiyama. Finally, we discuss the theory of skew detection and the different approaches taken to solve the problem of skew in documents. The skew correction algorithm we propose has been shown to he extremely fast, with run times averaging under 0.25 CPU seconds to calculate the angle on a DEC 5000/20 workstation.</div>
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A Document skew detection method using the hough transform

A Document skew detection method using the hough transform

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