Consideration of the Word's Neighborhood in GATs for Information Extraction in Semi-structured Documents.

Most administrative documents take a semi-structured form (invoices, payslips, etc.). Extracting information from this type of document is still challenging because of the variability of its structure brought about by the change of layout style of the different administrations. In this work, we try to face this type of variation by using a multi-layer Graph Attention Network (GAT). We propose a general structure of a semi-structured document. Based on this latter, we adopt a star sub-graph to exploit the surrounding context of words, allowing neighboring words to help locate the searched words and rank them. The GAT makes it possible to exploit this type of neighborhood and to highlight important neighboring words likely to be better identified. Each graph node contains at the same time textual and visual features. We experiment the multi-layer GAT on three different datasets: invoices and payslips (generated artificially), and receipts (issued from SROIE ICDAR competition). For the later dataset, we get an important F1 score of 0.892.