Document Image Segmentation Using a 2D Conditional Random Field Model

Stéphane Nicolas (1) , Julien Dardenne (2, 3) , Thierry Paquet (1) , Laurent Heutte (1)

1 DocApp - LITIS - Equipe Apprentissage
2 CREATIS - Centre de Recherche et d'Application en Traitement de l'Image et du Signal
3 AMPERE - Ampère

This work relates to the implementation of a 2D conditional random field model in the context of document image analysis. Our model makes it possible to take variability into account and to integrate contextual knowledge, while taking benefit from machine learning techniques. Experiments on handwritten drafts of Flaubert show that these models provide interesting solutions.

Mots clés: conditional random fields; layout analysis; document image analysis; segmentation; markovian models

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Stéphane Nicolas, Julien Dardenne, Thierry Paquet, Laurent Heutte. Document Image Segmentation Using a 2D Conditional Random Field Model. 9th International Conference on Document Analysis and Recognition, ICDAR'2007, Sep 2007, Curitiba, Brazil. pp: 407-411. ⟨hal-00509193⟩