Slant estimation algorithm for OCR systems
Identifieur interne : 001C83 ( Main/Merge ); précédent : 001C82; suivant : 001C84Slant estimation algorithm for OCR systems
Auteurs : E. Kavallieratou [Grèce] ; N. Fakotakis ; G. KokkinakisSource :
- Pattern Recognition [ 0031-3203 ] ; 2001.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
A slant removal algorithm is presented based on the use of the vertical projection profile of word images and the Wigner-Ville distribution. The slant correction does not affect the connectivity of the word and the resulting words are natural. The evaluation of our algorithm was equally made by subjective and objective means. The algorithm has been tested in English and Modern Greek samples of more than 500 writers, taken from the databases IAM-DB and GRUHD. The extracted results are natural, and almost always improved with respect to the original image, even in the case of variant-slanted writing. The performance of an existed character recognition system showed an increase of up to 9% for the same data, while the training time cost was significantly reduced. Due to its simplicity, this algorithm can be easily incorporated into any optical character recognition system. © 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
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Pascal:02-0029539Le document en format XML
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<term>Theory</term>
<term>Wigner-Ville distribution</term>
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<term>Traitement image</term>
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<term>Reconnaissance optique caractère</term>
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<front><div type="abstract" xml:lang="en">A slant removal algorithm is presented based on the use of the vertical projection profile of word images and the Wigner-Ville distribution. The slant correction does not affect the connectivity of the word and the resulting words are natural. The evaluation of our algorithm was equally made by subjective and objective means. The algorithm has been tested in English and Modern Greek samples of more than 500 writers, taken from the databases IAM-DB and GRUHD. The extracted results are natural, and almost always improved with respect to the original image, even in the case of variant-slanted writing. The performance of an existed character recognition system showed an increase of up to 9% for the same data, while the training time cost was significantly reduced. Due to its simplicity, this algorithm can be easily incorporated into any optical character recognition system. © 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.</div>
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