OcrV1, Main, Exploration, bibRecord, 000559

Improved Document Image Segmentation Algorithm using Multiresolution Morphology

Identifieur interne : 000559 ( Main/Exploration ); précédent : 000558; suivant : 000560

Improved Document Image Segmentation Algorithm using Multiresolution Morphology

Auteurs : SYED SAQIB BUKHARI [Allemagne] ; Faisal Shafait [Allemagne] ; Thomas M. Breuel [Allemagne]

Source :

Proceedings of SPIE, the International Society for Optical Engineering [ 0277-786X ] ; 2011.

RBID : Pascal:11-0278984

Descripteurs français

Pascal (Inist)
- Imagerie, Algorithme, Segmentation image, Traitement image, Traitement image document, Analyse multirésolution, Reconnaissance optique caractère, Précision, Marché concurrentiel, 0130C, 4230, 0705P, 4230V.

English descriptors

KwdEn :
- Accuracy, Algorithms, Document image processing, Image processing, Image segmentation, Imagery, Multiresolution analysis, Open market, Optical character recognition.

Abstract

Page segmentation into text and non-text elements is an essential preprocessing step before optical character recognition (OCR) operation. In case of poor segmentation, an OCR classification engine produces garbage characters due to the presence of non-text elements. This paper describes modifications to the text/non-text segmentation algorithm presented by Bloomberg,^l which is also available in his open-source Leptonica library.² The modifications result in significant improvements and achieved better segmentation accuracy than the original algorithm for UW-III, UNLV, ICDAR 2009 page segmentation competition test images and circuit diagram datasets.

Affiliations:

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

to stream PascalFrancis, to step Corpus: 000136
to stream PascalFrancis, to step Curation: 000637
to stream PascalFrancis, to step Checkpoint: 000113
to stream Main, to step Merge: 000565
to stream Main, to step Curation: 000559

Le document en format XML

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<front><div type="abstract" xml:lang="en">Page segmentation into text and non-text elements is an essential preprocessing step before optical character recognition (OCR) operation. In case of poor segmentation, an OCR classification engine produces garbage characters due to the presence of non-text elements. This paper describes modifications to the text/non-text segmentation algorithm presented by Bloomberg,<sup>l</sup>
 which is also available in his open-source Leptonica library.<sup>2</sup>
 The modifications result in significant improvements and achieved better segmentation accuracy than the original algorithm for UW-III, UNLV, ICDAR 2009 page segmentation competition test images and circuit diagram datasets.</div>
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Improved Document Image Segmentation Algorithm using Multiresolution Morphology

Improved Document Image Segmentation Algorithm using Multiresolution Morphology

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