Table detection in invoice documents by graph neural networks

Pau Riba, Anjan Dutta, Lutz Goldmann, Alicia Fornes, Oriol Ramos, Josep Llados

Research output: Book/ReportProceedingResearchpeer-review

69 Citations (Scopus)


Tabular structures in documents offer a complementary dimension to the raw textual data, representing logical or quantitative relationships among pieces of information. In digital mail room applications, where a large amount of administrative documents must be processed with reasonable accuracy, the detection and interpretation of tables is crucial. Table recognition has gained interest in document image analysis, in particular in unconstrained formats (absence of rule lines, unknown information of rows and columns). In this work, we propose a graph-based approach for detecting tables in document images. Instead of using the raw content (recognized text), we make use of the location, context and content type, thus it is purely a structure perception approach, not dependent on the language and the quality of the text reading. Our framework makes use of Graph Neural Networks (GNNs) in order to describe the local repetitive structural information of tables in invoice documents. Our proposed model has been experimentally validated in two invoice datasets and achieved encouraging results. Additionally, due to the scarcity of benchmark datasets for this task, we have contributed to the community a novel dataset derived from the RVL-CDIP invoice data. It will be publicly released to facilitate future research.

Original languageEnglish
Number of pages6
Publication statusPublished - Sept 2019

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
ISSN (Print)1520-5363


  • Administrative Documents
  • Detection
  • Geometric Deep Learning
  • Graph Neural Network
  • Graph Representations


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