Skip to Main content Skip to Navigation
Book sections

Administrative Document Analysis and Structure

Abdel Belaïd 1 Vincent Poulain d'Andecy 2 Hatem Hamza 2 yolande Belaïd 1 
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : This chapter reports our knowledge about the analysis and recognition of scanned administrative documents. Regarding essentially the administrative paper flow with new and continuous arrivals, all the conventional techniques reserved to static databases modeling and recognition are doomed to failure. For this purpose, a new technique based on the experience was investigated giving very promising results. This technique is related to the case-based reasoning already used in data mining and various problems of machine learning. After the presentation of the context related to the administrative document flow and its requirements in a real time processing, we present a case based reasonning for invoice processing. The case corresponds to the co-existence of a problem and its solution. The problem in an invoice corresponds to a local structure such as the keywords of an address or the line patterns in the amounts table, while the solution is related to their content. This problem is then compared to a document case base using graph probing. For this purpose, we proposed an improvement of an already existing neural network called Incremental Growing Neural Gas
Complete list of metadata

Cited literature [48 references]  Display  Hide  Download
Contributor : Abdel Belaid Connect in order to contact the contributor
Submitted on : Friday, March 25, 2011 - 10:07:59 AM
Last modification on : Friday, February 26, 2021 - 3:28:07 PM
Long-term archiving on: : Sunday, June 26, 2011 - 2:37:45 AM


Files produced by the author(s)




Abdel Belaïd, Vincent Poulain d'Andecy, Hatem Hamza, yolande Belaïd. Administrative Document Analysis and Structure. Marenglen Biba and Fatos Xhafa. Learning Structure and Schemas from Documents, 375, Springer Verlag, pp.51-72, 2011, Studies in Computational Intelligence, 978-3-642-22912-1. ⟨10.1007/978-3-642-22913-8⟩. ⟨inria-00579833⟩



Record views


Files downloads