Visual Segmentation for Information Extraction from Heterogeneous Visually Rich Documents

Physical and digital documents often contain visually rich information. With such information, there is no strict ordering or positioning in the document where the data values must appear. Along with textual cues, these documents often also rely on salient visual features to define distinct semantic boundaries and augment the information they disseminate. When performing information extraction (IE), traditional techniques fall short, as they use a text-only representation and do not consider the visual cues inherent to the layout of these documents. We propose VS2, a generalized approach for information extraction from heterogeneous visually rich documents. There are two major contributions of this work. First, we propose a robust segmentation algorithm that decomposes a visually rich document into a bag of visually isolated but semantically coherent areas, called logical blocks. Document type agnostic low-level visual and semantic features are used in this process. Our second contribution is a distantly supervised search-and-select method for identifying the named entities within these documents by utilizing the context boundaries defined by these logical blocks. Experimental results on three heterogeneous datasets suggest that the proposed approach significantly outperforms its text-only counterparts on all datasets. Comparing it against the state-of-the-art methods also reveal that VS2 performs comparably or better on all datasets.