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For understanding generic documents, information like font sizes, columnlayout, and generally the positioning of words may carry semantic informationthat is crucial for solving a downstream document intelligence task. Our novelBERTgrid, which is based on Chargrid by Katti et al. (2018), represents adocument as a grid of contextualized word piece embedding vectors, therebymaking its spatial structure and semantics accessible to the processing neuralnetwork. The contextualized embedding vectors are retrieved from a BERTlanguage model. We use BERTgrid in combination with a fully convolutionalnetwork on a semantic instance segmentation task for extracting fields frominvoices. We demonstrate its performance on tabulated line item and documentheader field extraction.

BERTgrid: Contextualized Embedding for 2D Document Representation and Understanding