This paper proposes a new method for labelling the logical structures of document images. The system starts with digitised images of paper documents, performs a physical layout analysis, runs an OCR and finally exploits the OCR's outputs to find the meaning of each block of text (i.e. assigns labels like "Title", "Author", etc.). The method is an extension of our previous work where a classifier, the perceptive neural network, has been developed to be an analogy of the human perception. We introduce in this connectionist model a temporal dimension by the use of a time-delay neural network with local representation. During the recognition stage, the system performs several recognition cycles and corrections, while keeping track and reusing the previous outputs. This dynamic classifier allows then a better handling of noise and segmentation errors. The experiments have been carried out on two datasets: the publicMARGcontaining more than 1,500 front pages of scientific papers with four zones of interest and another one composed of documents from the Siggraph 2003 conference, where 21 logical structures have been identified. The error rate on MARG is less than 2.5% and 7.3% on the Siggraph dataset.

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{{Explor lien
   |wiki=    Wicri/Lorraine
   |area=    InforLorV4
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   |étape=   Checkpoint
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   |clé=     Hal:inria-00579825
   |texte=   Labelling logical structures of document images using a dynamic perceptive neural network
}}