Adaptive Detection of Missed Text Areas in OCR Outputs: Application to the Automatic Assessment of OCR quality in Mass Digitization projects

Ahmed Ben Salah 1, 2 Nicolas Ragot 3 Thierry Paquet 1
1 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
2 BnF_DSC - Bibliothèque nationale de France, Département de la Conservation
3 LIFAT - Laboratoire d'Informatique Fondamentale et Appliquée de Tours
Abstract : 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 nd errors in OCR outputs that occur because of an accumulation of several errors at di erent 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 veri cation 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 les outputs (produced by the service providers) to the images of the document.
Mots-clés : OCR results assessment Text detection Texture characterization
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Conference papers
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Ahmed Ben Salah, Nicolas Ragot, Thierry Paquet. 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, Feb 2013, SAN FRANCISCO, United States. pp.110-122, ⟨10.1117/12.2003733⟩. ⟨hal-00820564⟩

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