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We introduce a novel type of text representation that preserves the 2D layoutof a document. This is achieved by encoding each document page as atwo-dimensional grid of characters. Based on this representation, we present ageneric document understanding pipeline for structured documents. This pipelinemakes use of a fully convolutional encoder-decoder network that predicts asegmentation mask and bounding boxes. We demonstrate its capabilities on aninformation extraction task from invoices and show that it significantlyoutperforms approaches based on sequential text or document images.

Chargrid: Towards Understanding 2D Documents