  We introduce a novel approach for scanned document representation to perform
field extraction. It allows the simultaneous encoding of the textual, visual
and layout information in a 3-axis tensor used as an input to a segmentation
model. We improve the recent Chargrid and Wordgrid \cite{chargrid} models in
several ways, first by taking into account the visual modality, then by
boosting its robustness in regards to small datasets while keeping the
inference time low. Our approach is tested on public and private document-image
datasets, showing higher performances compared to the recent state-of-the-art
methods.
