TitreAdaptive detection of missed text areas in OCR outputs: application to the automatic assessment of OCR quality in mass digitization projects
Type de publicationArticle de revue
Année de publication2013
AuteursAhmed Ben Salah, Nicolas Ragot, Thierry Paquet
JournalDocument Recognition and Retrieval XX. Proceedings of the SPIE
Volume8658
Résumé

The French National Library (BnF) has launched many mass digitization projects in order to give access to its collection. The indexation of digital documents on Gallica (digital library of the BnF) is done through their textual content obtained thanks to service providers that use Optical Character Recognition softwares (OCR). OCR softwares have become increasingly complex systems composed of several subsystems dedicated to the analysis and the recognition of the elements in a page. However, the reliability of these systems is always an issue at stake. Indeed, in some cases, we can find errors in OCR outputs that occur because of an accumulation of several errors at different levels in the OCR process. One of the frequent errors in OCR outputs is the missed text components. The presence of such errors may lead to severe defects in digital libraries. In this paper, we investigate the detection of missed text components to control the OCR results from the collections of the French National Library. Our verification approach uses local information inside the pages based on Radon transform descriptors and Local Binary Patterns descriptors (LBP) coupled with OCR results to control their consistency. The experimental results show that our method detects 84.15% of the missed textual components, by comparing the OCR ALTO files outputs (produced by the service providers) to the images of the document.

URLhttp://dx.doi.org/10.1117/12.2003733
DOI10.1117/12.2003733
Champ de recherche: 
adaptive detection of missed text areas in ocr outputs application to the automatic assessment of ocr quality in mass digitization projects document recognition and retrieval xx proceedings of the spie pthe french national library bnf has launched many mass digitization projects in order to give access to its collection the indexation of digital documents on gallica digital library of the bnf is done through their textual content obtained thanks to service providers that use optical character recognition softwares ocr ocr softwares have become increasingly complex systems composed of several subsystems dedicated to the analysis and the recognition of the elements in a page however the reliability of these systems is always an issue at stake indeed in some cases we can find errors in ocr outputs that occur because of an accumulation of several errors at different levels in the ocr process one of the frequent errors in ocr outputs is the missed text components the presence of such errors may lead to severe defects in digital libraries in this paper we investigate the detection of missed text components to control the ocr results from the collections of the french national library our verification approach uses local information inside the pages based on radon transform descriptors and local binary patterns descriptors lbp coupled with ocr results to control their consistency the experimental results show that our method detects 8415 of the missed textual components by comparing the ocr alto files outputs produced by the service providers to the images of the documentp httpdxdoiorg101117122003733 8658 101117122003733 ahmed ben salah nicolas ragot thierry paquet
Retour en haut de page