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OCR Performance Prediction using a Bag of Allographs and Support Vector Regression

Identifieur interne : 000074 ( Main/Exploration ); précédent : 000073; suivant : 000075

OCR Performance Prediction using a Bag of Allographs and Support Vector Regression

Auteurs : Tapan Bhowmik ; Thierry Paquet [France] ; Nicolas Ragot [France]

Source :

RBID : Hal:hal-01085002

English descriptors

Abstract

In this paper, we describe a novel and simple technique for prediction of OCR results without using any OCR. The technique uses a bag of allographs to characterize textual components. Then a support vector regression (SVR) technique is used to build a predictor based on the bag of allographs. The performance of the system is evaluated on a corpus of historical documents. The proposed technique produces correct prediction of OCR results on training and test documents within the range of standard deviation of 4.18% and 6.54% respectively. The proposed system has been designed as a tool to assist selection of corpora in libraries and specify the typical performance that can be expected on the selection.

Url:
DOI: 10.1109/DAS.2014.72


Affiliations:


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


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