Adaptative quality control of digital documents in mass digitization projects
Identifieur interne : 000244 ( Main/Exploration ); précédent : 000243; suivant : 000245Adaptative quality control of digital documents in mass digitization projects
Auteurs : Ahmed Ben Salah [France]Source :
Descripteurs français
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- Wicri :
English descriptors
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Abstract
This work focuses on the assessment of characters recognition results produced automatically by optical character recognition software (OCR on mass digitization projects. The goal is to design a global control system robust enough to deal with BnF documents collection. This collection includes old documents which are difficult to be treated by OCR. We designed a word detection system to detect missed words defects in OCR results, and a words recognition rate estimator to assess the quality of word recognition results performed by OCR.We create two kinds of descriptors to characterize OCR outputs. Image descriptors to characterize page segmentation results and cross alignment descriptors to characterize the quality of word recognition results. Furthermore, we adapt our learning process to make an adaptive decision or prediction systems. We evaluated our control systems on real images selected randomly from BnF collection. The mmissed word detection system detects 84.15% of words omitted by the OCR with a precision of 94.73%. The experiments performed also showed that 80% of the documents of word recognition rate less than 98% are detected with an accuracy of 92%. It can also automatically detect 45% of the material having a recognition rate less than 70% with greater than 92% accuracy.
Url:
Affiliations:
- France
- Centre-Val de Loire, Haute-Normandie, Région Centre, Région Normandie
- Le Havre, Rouen, Tours
- Centre Val de Loire Université, Université François-Rabelais de Tours, Université de Rouen, Université du Havre
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Le document en format XML
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Tours</orgName><desc><address><addrLine>60 rue du Plat d'Étain, 37020 Tours cedex 1 </addrLine><country key="FR"></country></address><ref type="url">http://www.univ-tours.fr</ref></desc></org></tutelle><tutelle active="#struct-300408" type="indirect"><org type="institution" xml:id="struct-300408" status="VALID"><orgName>Polytech'Tours</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Le Havre</settlement><region type="region" nuts="2">Région Normandie</region><region type="old region" nuts="2">Haute-Normandie</region></placeName><orgName type="university">Université du Havre</orgName><placeName><settlement type="city">Rouen</settlement><region type="region" nuts="2">Région Normandie</region><region type="old region" nuts="2">Haute-Normandie</region></placeName><orgName type="university">Université de Rouen</orgName><placeName><settlement type="city">Tours</settlement><region type="old region" nuts="2">Région Centre</region><region type="region" nuts="2">Centre-Val de Loire</region></placeName><orgName type="university">Université François-Rabelais de Tours</orgName><orgName type="institution" wicri:auto="newGroup">Centre Val de Loire Université</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Optical Character Recognition</term><term>Quality Assessment</term><term>Segmentation defects</term><term>Texture Characterization</term></keywords><keywords scheme="mix" xml:lang="fr"><term>Analyse de texture</term><term>Classification</term><term>Erreur de segmentation</term><term>Prédiction de performances</term><term>Reconnaissance de caractères</term><term>Reconnaissance optique de caractères</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Classification</term><term>Reconnaissance optique de caractères</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This work focuses on the assessment of characters recognition results produced automatically by optical character recognition software (OCR on mass digitization projects. The goal is to design a global control system robust enough to deal with BnF documents collection. This collection includes old documents which are difficult to be treated by OCR. We designed a word detection system to detect missed words defects in OCR results, and a words recognition rate estimator to assess the quality of word recognition results performed by OCR.We create two kinds of descriptors to characterize OCR outputs. Image descriptors to characterize page segmentation results and cross alignment descriptors to characterize the quality of word recognition results. Furthermore, we adapt our learning process to make an adaptive decision or prediction systems. We evaluated our control systems on real images selected randomly from BnF collection. The mmissed word detection system detects 84.15% of words omitted by the OCR with a precision of 94.73%. The experiments performed also showed that 80% of the documents of word recognition rate less than 98% are detected with an accuracy of 92%. It can also automatically detect 45% of the material having a recognition rate less than 70% with greater than 92% accuracy.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Centre-Val de Loire</li><li>Haute-Normandie</li><li>Région Centre</li><li>Région Normandie</li></region><settlement><li>Le Havre</li><li>Rouen</li><li>Tours</li></settlement><orgName><li>Centre Val de Loire Université</li><li>Université François-Rabelais de Tours</li><li>Université de Rouen</li><li>Université du Havre</li></orgName></list><tree><country name="France"><region name="Région Normandie"><name sortKey="Ben Salah, Ahmed" sort="Ben Salah, Ahmed" uniqKey="Ben Salah A" first="Ahmed" last="Ben Salah">Ahmed Ben Salah</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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