Entity Matching in OCRed Documents with Redundant Databases
Identifieur interne : 000027 ( Main/Exploration ); précédent : 000026; suivant : 000028Entity Matching in OCRed Documents with Redundant Databases
Auteurs : Nihel Kooli [France] ; Abdel Belaïd [France]Source :
English descriptors
Abstract
This paper presents an entity recognition approach on documents recognized by OCR (Optical Character Recognition). The recognition is formulated as a task of matching entities in a database with their representations in a document. A pre-processing step of entity resolution is performed on the database to provide a better representation of the entities. For this, a statistical model based on record linkage and record merge phases is used. Furthermore, documents recognized by OCR can contain noisy data and altered structure. An adapted method is proposed to retrieve the entities from their structures by tolerating possible OCR errors. A modified version of EROCS is applied to this problem by adapting the notion of segments to blocks provided by the OCR. It handles document segments to match the document to its corresponding entities. For efficiency, a process of data labeling in the document is applied in order to filter the compared entities and segments. The evaluation on business documents shows a significant improvement of matching rates compared to those of EROCS.
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DOI: 10.5220/0005177301650172
Affiliations:
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Le document en format XML
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The recognition is formulated as a task of matching entities in a database with their representations in a document. A pre-processing step of entity resolution is performed on the database to provide a better representation of the entities. For this, a statistical model based on record linkage and record merge phases is used. Furthermore, documents recognized by OCR can contain noisy data and altered structure. An adapted method is proposed to retrieve the entities from their structures by tolerating possible OCR errors. A modified version of EROCS is applied to this problem by adapting the notion of segments to blocks provided by the OCR. It handles document segments to match the document to its corresponding entities. For efficiency, a process of data labeling in the document is applied in order to filter the compared entities and segments. The evaluation on business documents shows a significant improvement of matching rates compared to those of EROCS.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Lorraine</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Lorraine"><name sortKey="Kooli, Nihel" sort="Kooli, Nihel" uniqKey="Kooli N" first="Nihel" last="Kooli">Nihel Kooli</name></region><name sortKey="Belaid, Abdel" sort="Belaid, Abdel" uniqKey="Belaid A" first="Abdel" last="Belaïd">Abdel Belaïd</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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