 @conference {4133,
	title = {OCR performance prediction using cross-OCR alignement},
	booktitle = {ICDAR},
	year = {2015},
	month = {2015/08/22},
	author = {Ahmed Ben Salah and Jean-Philippe Moreux and Nicolas Ragot and Thierry Paquet}
}
@conference {5322,
	title = {OCR performance prediction using cross-OCR alignment},
	booktitle = {13th International Conference on Document Analysis and Recognition (ICDAR 2015)},
	year = {2015},
	month = {2015/08/24},
	address = {Nancy, Palais des congr{\`e}s},
	abstract = {<p>Since 2006 the national library of France (BnF) has developed many mass digitization projects on its collections. The indexation of digital documents on Gallica (the digital library of the BnF) is done through their textual content obtained thanks to service providers that use Optical Character Recognition software (OCR). The modern technologies of OCR achieve good performances on modern documents produced with uniform layout and known fonts. However, for old documents, OCR results are of lower quality. The OCR quality assessment is a real challenge for the BnF. On the one hand, due to the sequential architecture of OCR treatments, the identification of OCR errors sources is intractable. On the other hand, besides the word confidence, no additional quality information is reported in OCR outputs. In this paper, we present a study on OCR performance estimation aiming to control the quality of word transcriptions achieved by OCR. This quality assessment process has to operate without any comparison with ground truthed data. In this respect, our methodology relies on cross alignment of the OCR results with those of a secondary OCR called reference OCR. This secondary OCR provides uncertain but useful information that will be used as uncertain groundtruth. OCR performance is estimated using support vector regression. This predictor uses some global features computed on the cross-alignment results. The experimentations reported show that our estimate describes more faithfully the quality of OCR outputs than average word confidence scores that are computed by OCR. The proposed methodology can be adapted easily to various corpora by tuning the system using a training dataset of documents that have similar properties to those to be treated.</p>},
	keywords = {Historical documents digitization, Mass digitization projects, OCR quality assessment, Support Vector Regression (SVR)},
	url = {https://hal.archives-ouvertes.fr/hal-01191701},
	author = {Ahmed Ben Salah and Jean-Philippe Moreux and Nicolas Ragot and Thierry Paquet}
}
@conference {4134,
	title = {The digital documents quality control workflow at the BnF (operation, issue, improvement)},
	booktitle = {Archiving},
	year = {2013},
	month = {2013},
	author = {Ahmed Ben Salah and Jean-Philippe Moreux and Laurent Duplouy}
}
@inbook {2778,
	title = {Aide {\`a} la gestion des processus de num{\'e}risation en vue de l{\textquoteright}OCRisation des ouvrages},
	booktitle = {CORIA : Actes de la Journ{\'e}e Jeunes Chercheurs 2012},
	year = {2012},
	pages = {455-460},
	address = {Bordeaux},
	url = {http://www.asso-aria.org/coria/2012/455.pdf},
	author = {Ahmed Ben Salah}
}
@inbook {salah_prediction_2012,
	title = {Prediction of Selection Decision of Document Using Bibliographic Data at the National Library of France (BnF)},
	booktitle = {Archiving 2012, Copenhague, 12-15 juin 2012},
	year = {2012},
	pages = {135{\textendash}140},
	publisher = {Society for Imaging Sciences and Technology},
	organization = {Society for Imaging Sciences and Technology},
	address = {Copenhague},
	abstract = {<p>The selection process of the documents is a very important step in mass digitization projects. This is especially true at the BnF, where the digitization should include or not OCRization depending on the OCR results expected. Consequently, the selection task is very complex and time consuming due to the number of documents to be processed and the diversity of the selection criteria to consider. Trying to improve and simplify this task by automation, we studied the relationship between bibliographic data and the selection decisions of documents. We used two statistical analysis: a factor analysis of correspondence and a multiple correspondence analysis. Our analysis has shown that, for example, the documents in format "4 or GR FOL" and edited "between 1961 and 1990" in Morocco are more likely to be "Selected". However, the documents in format "16 or 8" and edited "between 1871 and 1800" in English or Spanish have a greater chance to be "Not Selected".</p>},
	isbn = {978-0-89208-300-8},
	url = {http://www.imaging.org/IST/store/epub.cfm?abstrid=45326},
	author = {Ahmed Ben Salah and Genevi{\`e}ve Cron and Nicolas Ragot and Thierry Paquet}
}
