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Communication Dans Un Congrès Année : 2014

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

Résumé

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 The performance of the system is evaluated on a corpus of historical documents.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.
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Dates et versions

hal-01085002 , version 1 (20-11-2014)

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Citer

Tapan Kumar Bhowmik, Thierry Paquet, Nicolas Ragot. OCR Performance Prediction using a Bag of Allographs and Support Vector Regression. International Workshop on Document Analysis Systems, Jean-Yves Ramel, Marcus Liwicki, Apr 2014, Tours, France. pp.202-206, ⟨10.1109/DAS.2014.72⟩. ⟨hal-01085002⟩
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