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DOCUMENT IMAGE AND ZONE CLASSIFICATION THROUGH INCREMENTAL LEARNING

Mohamed-Rafik Bouguelia 1 Yolande Belaïd 1 Abdel Belaïd 1
1 READ - Recognition of writing and analysis of documents
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : We present an incremental learning method for document image and zone classification. We consider an industrial context where the system faces a large variability of digitized administrative documents that become available progressively over time. Each new incoming document is segmented into physical regions (zones) which are classified according to a zone-model. We represent the document by means of its classified zones and we classify the document according to a document-model. The classification relies on a reject utility in order to reject ambiguous zones or documents. Models are updated by incrementally learning each new document and its extracted zones. We validate the method on real administrative document images and we achieve a recognition rate of more than 92%.
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https://hal.inria.fr/hal-00865765
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Submitted on : Wednesday, September 25, 2013 - 10:02:43 AM
Last modification on : Friday, January 15, 2021 - 5:42:02 PM
Long-term archiving on: : Friday, April 7, 2017 - 2:33:39 AM

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Mohamed-Rafik Bouguelia, Yolande Belaïd, Abdel Belaïd. DOCUMENT IMAGE AND ZONE CLASSIFICATION THROUGH INCREMENTAL LEARNING. International Conference on Image Processing (ICIP), Sep 2013, Melbourne, Australia. pp.4230-4234. ⟨hal-00865765⟩

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