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Key Information Extraction (KIE) is aimed at extracting structuredinformation (e.g. key-value pairs) from form-style documents (e.g. invoices),which makes an important step towards intelligent document understanding.Previous approaches generally tackle KIE by sequence tagging, which facesdifficulty to process non-flatten sequences, especially for table-text mixeddocuments. These approaches also suffer from the trouble of pre-defining afixed set of labels for each type of documents, as well as the label imbalanceissue. In this work, we assume Optical Character Recognition (OCR) has beenapplied to input documents, and reformulate the KIE task as a region predictionproblem in the two-dimensional (2D) space given a target field. Following thisnew setup, we develop a new KIE model named Region-based Document Understanding(RDU) that takes as input the text content and corresponding coordinates of adocument, and tries to predict the result by localizing a bounding-box-likeregion. Our RDU first applies a layout-aware BERT equipped with a soft layoutattention masking and bias mechanism to incorporate layout information into therepresentations. Then, a list of candidate regions is generated from therepresentations via a Region Proposal Module inspired by computer vision modelswidely applied for object detection. Finally, a Region Categorization Moduleand a Region Selection Module are adopted to judge whether a proposed region isvalid and select the one with the largest probability from all proposed regionsrespectively. Experiments on four types of form-style documents show that ourproposed method can achieve impressive results. In addition, our RDU model canbe trained with different document types seamlessly, which is especiallyhelpful over low-resource documents.

RDU: A Region-based Approach to Form-style Document Understanding