  Extracting information from documents usually relies on natural language
processing methods working on one-dimensional sequences of text. In some cases,
for example, for the extraction of key information from semi-structured
documents, such as invoice-documents, spatial and formatting information of
text are crucial to understand the contextual meaning. Convolutional neural
networks are already common in computer vision models to process and extract
relationships in multidimensional data. Therefore, natural language processing
models have already been combined with computer vision models in the past, to
benefit from e.g. positional information and to improve performance of these
key information extraction models. Existing models were either trained on
unpublished data sets or on an annotated collection of receipts, which did not
focus on PDF-like documents. Hence, in this research project a template-based
document generator was created to compare state-of-the-art models for
information extraction. An existing information extraction model "Chargrid"
(Katti et al., 2019) was reconstructed and the impact of a bounding box
regression decoder, as well as the impact of an NLP pre-processing step was
evaluated for information extraction from documents. The results have shown
that NLP based pre-processing is beneficial for model performance. However, the
use of a bounding box regression decoder increases the model performance only
for fields that do not follow a rectangular shape.
