Information extraction in unconstrained handwritten documents: application to automatic processing of handwritten incoming mail
Identifieur interne : 000567 ( Main/Exploration ); précédent : 000566; suivant : 000568Information extraction in unconstrained handwritten documents: application to automatic processing of handwritten incoming mail
Auteurs : S. Thomas [France]Source :
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Abstract
Despite the avenment of our world into the digital era, a large amount of handwritten documents continue to be exchanged, forcing our companies and administrations to cope with the processing of masses of documents. Automatic processing of these documents requires access to an unknown but relevant part of their content, and implies taking into account three key points: the document segmentation into relevant entities, their recognition and the rejection of irrelevant entities. Contrary to traditional approaches (full documents reading or keyword detection), all processes are parallelized leading to an information extraction approach. The first contribution of the present work is the design of a generic text line model for information extraction purpose and the implementation of a complete system based on Hidden Markov Models (HMM) constrained by this model. In one pass, the recognition module seeks to discriminate relevant information, characterized by a set of alphabetic, numeric or alphanumeric queries, with the irrelevant information, characterized by a filler model. A second contribution concerns the improvement of the local frame discrimination by using a deep neural network. This allows one to infer high-level representation for the frames and thus automate the feature extraction process. These result is a complete, generic and industrially system, responding to emerging needs in the field of handwritten document automatic reading: the extraction of complex information in unconstrained documents.
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Automatic processing of these documents requires access to an unknown but relevant part of their content, and implies taking into account three key points: the document segmentation into relevant entities, their recognition and the rejection of irrelevant entities. Contrary to traditional approaches (full documents reading or keyword detection), all processes are parallelized leading to an information extraction approach. The first contribution of the present work is the design of a generic text line model for information extraction purpose and the implementation of a complete system based on Hidden Markov Models (HMM) constrained by this model. In one pass, the recognition module seeks to discriminate relevant information, characterized by a set of alphabetic, numeric or alphanumeric queries, with the irrelevant information, characterized by a filler model. A second contribution concerns the improvement of the local frame discrimination by using a deep neural network. This allows one to infer high-level representation for the frames and thus automate the feature extraction process. These result is a complete, generic and industrially system, responding to emerging needs in the field of handwritten document automatic reading: the extraction of complex information in unconstrained documents.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Haute-Normandie</li><li>Région Normandie</li></region><settlement><li>Le Havre</li><li>Rouen</li></settlement><orgName><li>Université de Rouen</li><li>Université du Havre</li></orgName></list><tree><country name="France"><region name="Région Normandie"><name sortKey="Thomas, S" sort="Thomas, S" uniqKey="Thomas S" first="S." last="Thomas">S. Thomas</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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