OcrV1, France, Analysis, bibRecord, 000163

Dynamic filters selection for textual document images denoising

Identifieur interne : 000163 ( France/Analysis ); précédent : 000162; suivant : 000164

Dynamic filters selection for textual document images denoising

Auteurs : Hubert Cecotti [Allemagne] ; Abdel Belaïd [France]

Source :

RBID : Hal:inria-00347215

Abstract

For a document class, one challenge in document restoration is to automatically find a set of filters, which are adapted to the degradation level of the images. Furthermore, it is important to know what filters and where they can be applied advantageously. In this paper, we present a multi classifiers solution for the extraction of linear filters. These filters are used for binarization and image denoising. The technique starts by clustering close pixels by K-means in as many clusters as filters. Each cluster is dedicated to a filter, which corresponds to a supervised neural network. These classifiers are trained according to a binarized image that is weighted function to erosion transformation effects. The presented method is compared to classical binarization techniques in literature. Its effect on the commercial OCR performances reaches a gain from 0,16% for Finereader7 and 1,06% for Omnipage14 for the recognition rate.

Url:

https://hal.inria.fr/inria-00347215

Affiliations:

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

to stream Hal, to step Corpus: 000041
to stream Hal, to step Curation: 000041
to stream Hal, to step Checkpoint: 000118
to stream Main, to step Merge: 000B13
to stream Main, to step Curation: 000B02
to stream Main, to step Exploration: 000B02
to stream France, to step Extraction: 000163

Links to Exploration step

Hal:inria-00347215

Le document en format XML

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<front><div type="abstract" xml:lang="en">For a document class, one challenge in document restoration is to automatically find a set of filters, which are adapted to the degradation level of the images. Furthermore, it is important to know what filters and where they can be applied advantageously. In this paper, we present a multi classifiers solution for the extraction of linear filters. These filters are used for binarization and image denoising. The technique starts by clustering close pixels by K-means in as many clusters as filters. Each cluster is dedicated to a filter, which corresponds to a supervised neural network. These classifiers are trained according to a binarized image that is weighted function to erosion transformation effects. The presented method is compared to classical binarization techniques in literature. Its effect on the commercial OCR performances reaches a gain from 0,16% for Finereader7 and 1,06% for Omnipage14 for the recognition rate.</div>
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{{Explor lien
   |wiki=    Ticri/CIDE
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   |clé=     Hal:inria-00347215
   |texte=   Dynamic filters selection for textual document images denoising
}}

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Dynamic filters selection for textual document images denoising

Dynamic filters selection for textual document images denoising

Source :

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