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This paper describes a segmentation method of continuous document flow. A document flow is a list of successive scanned pages, put in a production chain, representing several documents without explicit separation mark between them. To separate the documents for their recognition, it is needed to analyze the content of the successive pages and to  point  out  the  limit  pages  of  each  document.  The  method  proposed  here  is  similar  to  the  variable  horizon  models (VHM) or multi-grams used in speech recognition. It consists in maximizing the flow likelihood knowing all the Markov Models of the constituent elements. As the calculation of this likelihood on all the flow is NP-complete, the solution  consists  in  studying  them  in  windows ,of  reduced  observations.  The  first  results  obtained  on  homogeneous flows of invoices reaches more than 75% of precision and 90% of recall.

Segmentation of continuous document flow by a modified backward-forward algorithm